The sinking of the Titanic is one of the most infamous shipwrecks in history.
On April 15, 1912, during her maiden voyage, the widely considered “unsinkable” RMS Titanic sank after colliding with an iceberg. Unfortunately, there weren’t enough lifeboats for everyone onboard, resulting in the death of 1502 out of 2224 passengers and crew.
While there was some element of luck involved in surviving, it seems some groups of people were more likely to survive than others.
Build a predictive model that answers the question: “what sorts of people were more likely to survive?” using passenger data (ie name, age, gender, socio-economic class, etc).
# The other version of tensorflow does not work well with the codes taht I have used in this notebook, so make sure your libraries are the same as mine
!pip install keras==2.12.0
Requirement already satisfied: keras==2.12.0 in /usr/local/lib/python3.10/dist-packages (2.12.0)
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
import seaborn as sns
import random
# Suppress warnings from displaying to console
import warnings
warnings.filterwarnings("ignore")
# Removes the limit for the number of displayed columns
pd.set_option("display.max_columns", None)
# Sets the limit for the number of displayed rows
pd.set_option("display.max_rows", 200)
# To build models for prediction
from sklearn.model_selection import train_test_split, cross_val_score, KFold
from sklearn.preprocessing import StandardScaler, MinMaxScaler
from sklearn.svm import SVC
from sklearn.linear_model import LinearRegression, LogisticRegression, Ridge, Lasso, ElasticNet
from sklearn import metrics
from sklearn.metrics import confusion_matrix, classification_report, precision_recall_curve, recall_score, precision_score, f1_score, accuracy_score
from sklearn import tree
# To encode categorical variables
from sklearn.preprocessing import LabelEncoder
# For tuning the model
from sklearn.model_selection import GridSearchCV, RandomizedSearchCV
#AUC-ROC Tuning
from sklearn.metrics import roc_curve
from matplotlib import pyplot
# To check model performance
from sklearn.metrics import make_scorer,mean_squared_error, r2_score, mean_absolute_error
#Import tensorflow for deep learning
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Input, Dropout,BatchNormalization
from tensorflow.keras.wrappers.scikit_learn import KerasClassifier
from keras.optimizers import Adamax, Adam
from tensorflow.keras import backend
Import Dataset
titantrain = pd.read_csv('/content/drive/MyDrive/Colab Notebooks/Titanic/titanic_train.csv')
titantest = pd.read_csv('/content/drive/MyDrive/Colab Notebooks/Titanic//titanic_test.csv')
titansubmission = pd.read_csv('/content/drive/MyDrive/Colab Notebooks/Titanic//titanic_gender_submission.csv')
Create copy of each dataset
#This is done to protect the data, in case you accidentally deleted the data (better be safe than sorry)
titantrain_copy = titantrain.copy()
titantest_copy = titantest.copy()
titansubmission_copy = titansubmission.copy()
Seeing what each data looks like
#This is the data to be tested
titantest.head()
| PassengerId | Pclass | Name | Sex | Age | SibSp | Parch | Ticket | Fare | Cabin | Embarked | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 892 | 3 | Kelly, Mr. James | male | 34.5 | 0 | 0 | 330911 | 7.8292 | NaN | Q |
| 1 | 893 | 3 | Wilkes, Mrs. James (Ellen Needs) | female | 47.0 | 1 | 0 | 363272 | 7.0000 | NaN | S |
| 2 | 894 | 2 | Myles, Mr. Thomas Francis | male | 62.0 | 0 | 0 | 240276 | 9.6875 | NaN | Q |
| 3 | 895 | 3 | Wirz, Mr. Albert | male | 27.0 | 0 | 0 | 315154 | 8.6625 | NaN | S |
| 4 | 896 | 3 | Hirvonen, Mrs. Alexander (Helga E Lindqvist) | female | 22.0 | 1 | 1 | 3101298 | 12.2875 | NaN | S |
#data that will be used to train the model
titantrain.head()
| PassengerId | Survived | Pclass | Name | Sex | Age | SibSp | Parch | Ticket | Fare | Cabin | Embarked | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 0 | 3 | Braund, Mr. Owen Harris | male | 22.0 | 1 | 0 | A/5 21171 | 7.2500 | NaN | S |
| 1 | 2 | 1 | 1 | Cumings, Mrs. John Bradley (Florence Briggs Th... | female | 38.0 | 1 | 0 | PC 17599 | 71.2833 | C85 | C |
| 2 | 3 | 1 | 3 | Heikkinen, Miss. Laina | female | 26.0 | 0 | 0 | STON/O2. 3101282 | 7.9250 | NaN | S |
| 3 | 4 | 1 | 1 | Futrelle, Mrs. Jacques Heath (Lily May Peel) | female | 35.0 | 1 | 0 | 113803 | 53.1000 | C123 | S |
| 4 | 5 | 0 | 3 | Allen, Mr. William Henry | male | 35.0 | 0 | 0 | 373450 | 8.0500 | NaN | S |
#This is what our data to be submitted should look like
titansubmission.head()
| PassengerId | Survived | |
|---|---|---|
| 0 | 892 | 0 |
| 1 | 893 | 1 |
| 2 | 894 | 0 |
| 3 | 895 | 0 |
| 4 | 896 | 1 |
#This function will study the data's shape, datatype, number of null data and number of duplicate data.
def studydata(df):
print("Shape:")
print(df.shape)
print("\nInfo:")
print(df.info())
print("\nNull:")
print(df.isnull().sum())
print("\nDuplicates:")
print(df.duplicated().sum())
# print("\nHead:")
# print(df.head().T)
# print("\nTail:")
# print(df.tail().T)
studydata(titantrain)
Shape: (891, 12) Info: <class 'pandas.core.frame.DataFrame'> RangeIndex: 891 entries, 0 to 890 Data columns (total 12 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 PassengerId 891 non-null int64 1 Survived 891 non-null int64 2 Pclass 891 non-null int64 3 Name 891 non-null object 4 Sex 891 non-null object 5 Age 714 non-null float64 6 SibSp 891 non-null int64 7 Parch 891 non-null int64 8 Ticket 891 non-null object 9 Fare 891 non-null float64 10 Cabin 204 non-null object 11 Embarked 889 non-null object dtypes: float64(2), int64(5), object(5) memory usage: 83.7+ KB None Null: PassengerId 0 Survived 0 Pclass 0 Name 0 Sex 0 Age 177 SibSp 0 Parch 0 Ticket 0 Fare 0 Cabin 687 Embarked 2 dtype: int64 Duplicates: 0
#Getting percentage of null values
round(titantrain.isnull().sum()/len(titantrain)*100,2)
PassengerId 0.00 Survived 0.00 Pclass 0.00 Name 0.00 Sex 0.00 Age 19.87 SibSp 0.00 Parch 0.00 Ticket 0.00 Fare 0.00 Cabin 77.10 Embarked 0.22 dtype: float64
#To have an idea of what each column in the training data looks like
for i in titantrain:
print(titantrain[i].value_counts().sort_values(ascending = False))
print('-'*50)
PassengerId
1 1
13 1
14 1
3 1
4 1
..
886 1
887 1
888 1
889 1
891 1
Name: count, Length: 891, dtype: int64
--------------------------------------------------
Survived
0 549
1 342
Name: count, dtype: int64
--------------------------------------------------
Pclass
3 491
1 216
2 184
Name: count, dtype: int64
--------------------------------------------------
Name
Braund, Mr. Owen Harris 1
Saundercock, Mr. William Henry 1
Andersson, Mr. Anders Johan 1
Heikkinen, Miss. Laina 1
Futrelle, Mrs. Jacques Heath (Lily May Peel) 1
..
Rice, Mrs. William (Margaret Norton) 1
Montvila, Rev. Juozas 1
Graham, Miss. Margaret Edith 1
Johnston, Miss. Catherine Helen "Carrie" 1
Dooley, Mr. Patrick 1
Name: count, Length: 891, dtype: int64
--------------------------------------------------
Sex
male 577
female 314
Name: count, dtype: int64
--------------------------------------------------
Age
24.00 30
22.00 27
18.00 26
19.00 25
28.00 25
30.00 25
21.00 24
25.00 23
36.00 22
29.00 20
26.00 18
27.00 18
35.00 18
32.00 18
16.00 17
31.00 17
20.00 15
34.00 15
33.00 15
23.00 15
39.00 14
42.00 13
17.00 13
40.00 13
45.00 12
38.00 11
50.00 10
2.00 10
4.00 10
44.00 9
48.00 9
47.00 9
9.00 8
54.00 8
1.00 7
51.00 7
14.00 6
52.00 6
37.00 6
49.00 6
41.00 6
3.00 6
15.00 5
43.00 5
58.00 5
56.00 4
5.00 4
11.00 4
60.00 4
8.00 4
62.00 4
65.00 3
7.00 3
61.00 3
46.00 3
6.00 3
71.00 2
45.50 2
28.50 2
32.50 2
55.00 2
40.50 2
59.00 2
0.83 2
0.75 2
57.00 2
30.50 2
63.00 2
13.00 2
64.00 2
10.00 2
70.00 2
14.50 1
23.50 1
0.92 1
55.50 1
36.50 1
12.00 1
70.50 1
24.50 1
53.00 1
20.50 1
80.00 1
66.00 1
0.67 1
0.42 1
34.50 1
74.00 1
Name: count, dtype: int64
--------------------------------------------------
SibSp
0 608
1 209
2 28
4 18
3 16
8 7
5 5
Name: count, dtype: int64
--------------------------------------------------
Parch
0 678
1 118
2 80
5 5
3 5
4 4
6 1
Name: count, dtype: int64
--------------------------------------------------
Ticket
347082 7
1601 7
CA. 2343 7
3101295 6
CA 2144 6
..
2683 1
SOTON/O2 3101287 1
11774 1
392092 1
370376 1
Name: count, Length: 681, dtype: int64
--------------------------------------------------
Fare
8.0500 43
13.0000 42
7.8958 38
7.7500 34
26.0000 31
..
32.3208 1
8.3625 1
8.4333 1
25.5875 1
10.5167 1
Name: count, Length: 248, dtype: int64
--------------------------------------------------
Cabin
B96 B98 4
C23 C25 C27 4
G6 4
C22 C26 3
F33 3
F2 3
E101 3
D 3
D26 2
F4 2
C124 2
F G73 2
B58 B60 2
C52 2
D33 2
D20 2
D17 2
B28 2
C83 2
E25 2
C126 2
B22 2
C92 2
C2 2
E33 2
B51 B53 B55 2
C68 2
B57 B59 B63 B66 2
D35 2
B5 2
C78 2
C93 2
E8 2
D36 2
C123 2
E121 2
E44 2
B77 2
C125 2
B35 2
B18 2
E24 2
B49 2
C65 2
E67 2
B20 2
D56 1
E31 1
B30 1
B78 1
A6 1
C118 1
C103 1
E46 1
D10 D12 1
A19 1
A5 1
B4 1
C110 1
F E69 1
D7 1
D47 1
B19 1
A7 1
C49 1
A32 1
B80 1
A31 1
A34 1
B73 1
C7 1
D46 1
E50 1
E36 1
C54 1
C87 1
E34 1
C32 1
C91 1
E40 1
T 1
C128 1
D37 1
C106 1
B79 1
C82 1
C104 1
C111 1
E38 1
D21 1
E12 1
E63 1
A14 1
B37 1
C30 1
D15 1
B102 1
B94 1
A23 1
A24 1
C50 1
B42 1
D49 1
B71 1
C85 1
D50 1
B41 1
D9 1
E68 1
A10 1
C99 1
C101 1
A20 1
B50 1
A26 1
D48 1
D19 1
C86 1
A16 1
E58 1
C70 1
E17 1
D28 1
C47 1
E49 1
B86 1
C95 1
E10 1
B39 1
F G63 1
C62 C64 1
C90 1
C45 1
B38 1
B101 1
D45 1
C46 1
D30 1
D11 1
E77 1
F38 1
B3 1
D6 1
B82 B84 1
A36 1
B69 1
C148 1
Name: count, dtype: int64
--------------------------------------------------
Embarked
S 644
C 168
Q 77
Name: count, dtype: int64
--------------------------------------------------
Observations:
studydata(titantest)
Shape: (418, 11) Info: <class 'pandas.core.frame.DataFrame'> RangeIndex: 418 entries, 0 to 417 Data columns (total 11 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 PassengerId 418 non-null int64 1 Pclass 418 non-null int64 2 Name 418 non-null object 3 Sex 418 non-null object 4 Age 332 non-null float64 5 SibSp 418 non-null int64 6 Parch 418 non-null int64 7 Ticket 418 non-null object 8 Fare 417 non-null float64 9 Cabin 91 non-null object 10 Embarked 418 non-null object dtypes: float64(2), int64(4), object(5) memory usage: 36.0+ KB None Null: PassengerId 0 Pclass 0 Name 0 Sex 0 Age 86 SibSp 0 Parch 0 Ticket 0 Fare 1 Cabin 327 Embarked 0 dtype: int64 Duplicates: 0
#Getting percentage of null values
round(titantest.isnull().sum()/len(titantrain)*100,2)
PassengerId 0.00 Pclass 0.00 Name 0.00 Sex 0.00 Age 9.65 SibSp 0.00 Parch 0.00 Ticket 0.00 Fare 0.11 Cabin 36.70 Embarked 0.00 dtype: float64
#Seeing what the single null fare data looks like
titantest.loc[titantest['Fare'].isna()]
| PassengerId | Pclass | Name | Sex | Age | SibSp | Parch | Ticket | Fare | Cabin | Embarked | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 152 | 1044 | 3 | Storey, Mr. Thomas | male | 60.5 | 0 | 0 | 3701 | NaN | NaN | S |
Observations:
First off, let's convert the datatypes into the suitable ones first, and then analyze the numerical and categorical variables respectively.
titantrain.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 891 entries, 0 to 890 Data columns (total 12 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 PassengerId 891 non-null int64 1 Survived 891 non-null int64 2 Pclass 891 non-null int64 3 Name 891 non-null object 4 Sex 891 non-null object 5 Age 714 non-null float64 6 SibSp 891 non-null int64 7 Parch 891 non-null int64 8 Ticket 891 non-null object 9 Fare 891 non-null float64 10 Cabin 204 non-null object 11 Embarked 889 non-null object dtypes: float64(2), int64(5), object(5) memory usage: 83.7+ KB
#converting the following integer data into object data
titantrain['PassengerId'] = titantrain['PassengerId'].astype('object')
titantrain['Pclass'] = titantrain['Pclass'].astype('object')
titantrain['Survived'] = titantrain['Survived'].astype('object')
#Retrieving the names of numerical columns and categorical columns
num_cols = titantrain._get_numeric_data().columns
cat_cols = titantrain.select_dtypes(exclude='number').columns
print(num_cols)
print(cat_cols)
Index(['Age', 'SibSp', 'Parch', 'Fare'], dtype='object')
Index(['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Ticket', 'Cabin',
'Embarked'],
dtype='object')
# Countplot for categorical variables
def labeled_countplot(data,feature,perc = False, n = None, order = True):
total = len(data[feature])
count = data[feature].nunique()
#Changing size of the plot
if n is None:
plt.figure(figsize = (count + 1, 5)) #if n is not specified, then the size of the chart will be the according to number of features
else:
plt.figure(figsize = (n + 1, 5))
#Rotate the x labels
plt.xticks(rotation = 90)
#Create the countplot and assigning it to object
if order == False:
ax = sns.countplot(data = data,
x = feature,
palette = "Paired")
elif order == True:
ax = sns.countplot(data = data,
x = feature,
palette = "Paired",
order = data.groupby([feature])['PassengerId'].count().sort_values(ascending = False).index)
else:
ax = sns.countplot(data = data,
x = feature,
palette = "Paired",
order = order)
#Creating the labels
for p in ax.patches:
if perc == True:
label = "{:.1f}%".format(100*p.get_height()/total) #Gets the percentage value of the height
else:
label = p.get_height() # Just get the height without percentage
#Getting coordinates for the annotation
x = p.get_x() + p.get_width()/2
y = p.get_height()
#Coding the annotations
ax.annotate(label,(x,y),
ha = "center",
va = "center",
size = 12,
xytext = (0,5),
textcoords = "offset points")
plt.show()
#Histogram + Boxplot for Numerical Variables
def histogram_boxplot(data, feature, figsize=(12, 7), kde=False, bins=None, whis = 1.5,outliers = True, mean = True, median = True):
#Creating the subplot to place both plots in
f2, (ax_box2, ax_hist2) = plt.subplots(nrows = 2, # Number of rows of the subplot grid = 2
sharex = True, # x-axis will be shared among all subplots
gridspec_kw = {"height_ratios": (0.25, 0.75)}, #This sets the 2 subplots' height ratios, with top one taking 25% of the total figure
figsize = figsize) # Creating the 2 subplots
# Create Boxplot that shows mean
sns.boxplot(data = data,
x = feature,
whis = whis,
showfliers = outliers,
ax = ax_box2,
showmeans = True,
color = "orange")
# Create Histogram
sns.histplot(data = data,
x = feature,
kde = kde,
ax = ax_hist2,
bins = bins, #Since the bins cannot = non-integer, we need this second part of code
palette = "winter") if bins else sns.histplot(data = data,
x = feature,
kde = kde,
ax = ax_hist2)
# Add mean to the histogram
if mean == True:
ax_hist2.axvline(data[feature].mean(),
color = "green",
linestyle = "--")
else:
pass
# Add median to the histogram
if median == True:
ax_hist2.axvline(data[feature].median(),
color = "black",
linestyle = "-.")
else:
pass
#Print out the 5 summary for easier viewing
print(data[feature].describe())
# Function to plot stacked bar plots
def stacked_barplot(data, predictor, target):
count = data[predictor].nunique() #This pulls out the number of unique values in the column
sorter = data[target].value_counts().index[-1] #this tells you the least frequent value, as it sorts based on the frequency, and -1 means the least frequent
#Create the stacked barplot for understanding
tab1 = pd.crosstab(data[predictor],
data[target],
margins = True).sort_values(by = sorter, #Sort based on the least frequent value
ascending = False)
print(tab1)
print("-" * 120)
#Create the stacked barplot for visualizing
tab = pd.crosstab(data[predictor],
data[target],
normalize = "index").sort_values(by = sorter,
ascending = False)
tab.plot(kind = "bar",
stacked = True,
figsize = (count + 1, 5))
plt.legend(loc = "lower left",frameon = False,)
plt.legend(loc = "upper left", bbox_to_anchor = (1, 1))
plt.show()
histogram_boxplot(titantrain,'Age', kde = True)
count 714.000000 mean 29.699118 std 14.526497 min 0.420000 25% 20.125000 50% 28.000000 75% 38.000000 max 80.000000 Name: Age, dtype: float64
titantrain.loc[titantrain['Age'] < 1]
| PassengerId | Survived | Pclass | Name | Sex | Age | SibSp | Parch | Ticket | Fare | Cabin | Embarked | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 78 | 79 | 1 | 2 | Caldwell, Master. Alden Gates | male | 0.83 | 0 | 2 | 248738 | 29.0000 | NaN | S |
| 305 | 306 | 1 | 1 | Allison, Master. Hudson Trevor | male | 0.92 | 1 | 2 | 113781 | 151.5500 | C22 C26 | S |
| 469 | 470 | 1 | 3 | Baclini, Miss. Helene Barbara | female | 0.75 | 2 | 1 | 2666 | 19.2583 | NaN | C |
| 644 | 645 | 1 | 3 | Baclini, Miss. Eugenie | female | 0.75 | 2 | 1 | 2666 | 19.2583 | NaN | C |
| 755 | 756 | 1 | 2 | Hamalainen, Master. Viljo | male | 0.67 | 1 | 1 | 250649 | 14.5000 | NaN | S |
| 803 | 804 | 1 | 3 | Thomas, Master. Assad Alexander | male | 0.42 | 0 | 1 | 2625 | 8.5167 | NaN | C |
| 831 | 832 | 1 | 2 | Richards, Master. George Sibley | male | 0.83 | 1 | 1 | 29106 | 18.7500 | NaN | S |
Observations:
#Since the number of siblings and spouses are discrete values, we use the countplot to study this variable
labeled_countplot(titantrain,'SibSp', order = ['0','1','2','3','4','5','8'])
Observations:
#See if there are any trends with the cluster that has 8 siblings
titantrain.loc[titantrain['SibSp'] == 8]
| PassengerId | Survived | Pclass | Name | Sex | Age | SibSp | Parch | Ticket | Fare | Cabin | Embarked | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 159 | 160 | 0 | 3 | Sage, Master. Thomas Henry | male | NaN | 8 | 2 | CA. 2343 | 69.55 | NaN | S |
| 180 | 181 | 0 | 3 | Sage, Miss. Constance Gladys | female | NaN | 8 | 2 | CA. 2343 | 69.55 | NaN | S |
| 201 | 202 | 0 | 3 | Sage, Mr. Frederick | male | NaN | 8 | 2 | CA. 2343 | 69.55 | NaN | S |
| 324 | 325 | 0 | 3 | Sage, Mr. George John Jr | male | NaN | 8 | 2 | CA. 2343 | 69.55 | NaN | S |
| 792 | 793 | 0 | 3 | Sage, Miss. Stella Anna | female | NaN | 8 | 2 | CA. 2343 | 69.55 | NaN | S |
| 846 | 847 | 0 | 3 | Sage, Mr. Douglas Bullen | male | NaN | 8 | 2 | CA. 2343 | 69.55 | NaN | S |
| 863 | 864 | 0 | 3 | Sage, Miss. Dorothy Edith "Dolly" | female | NaN | 8 | 2 | CA. 2343 | 69.55 | NaN | S |
#Since all the siblings contain the surname 'Sage', we can see if there are any other passengers with 'Sage' in their name
titantrain[titantrain['Name'].astype('string').str.contains("Sage")]
| PassengerId | Survived | Pclass | Name | Sex | Age | SibSp | Parch | Ticket | Fare | Cabin | Embarked | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 159 | 160 | 0 | 3 | Sage, Master. Thomas Henry | male | NaN | 8 | 2 | CA. 2343 | 69.55 | NaN | S |
| 180 | 181 | 0 | 3 | Sage, Miss. Constance Gladys | female | NaN | 8 | 2 | CA. 2343 | 69.55 | NaN | S |
| 201 | 202 | 0 | 3 | Sage, Mr. Frederick | male | NaN | 8 | 2 | CA. 2343 | 69.55 | NaN | S |
| 324 | 325 | 0 | 3 | Sage, Mr. George John Jr | male | NaN | 8 | 2 | CA. 2343 | 69.55 | NaN | S |
| 641 | 642 | 1 | 1 | Sagesser, Mlle. Emma | female | 24.0 | 0 | 0 | PC 17477 | 69.30 | B35 | C |
| 792 | 793 | 0 | 3 | Sage, Miss. Stella Anna | female | NaN | 8 | 2 | CA. 2343 | 69.55 | NaN | S |
| 846 | 847 | 0 | 3 | Sage, Mr. Douglas Bullen | male | NaN | 8 | 2 | CA. 2343 | 69.55 | NaN | S |
| 863 | 864 | 0 | 3 | Sage, Miss. Dorothy Edith "Dolly" | female | NaN | 8 | 2 | CA. 2343 | 69.55 | NaN | S |
#It was also observed that all the siblings had the same ticket number, so we can see if anybody else had the same ticekt.
titantrain.loc[titantrain['Ticket'] == "CA. 2343"]
| PassengerId | Survived | Pclass | Name | Sex | Age | SibSp | Parch | Ticket | Fare | Cabin | Embarked | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 159 | 160 | 0 | 3 | Sage, Master. Thomas Henry | male | NaN | 8 | 2 | CA. 2343 | 69.55 | NaN | S |
| 180 | 181 | 0 | 3 | Sage, Miss. Constance Gladys | female | NaN | 8 | 2 | CA. 2343 | 69.55 | NaN | S |
| 201 | 202 | 0 | 3 | Sage, Mr. Frederick | male | NaN | 8 | 2 | CA. 2343 | 69.55 | NaN | S |
| 324 | 325 | 0 | 3 | Sage, Mr. George John Jr | male | NaN | 8 | 2 | CA. 2343 | 69.55 | NaN | S |
| 792 | 793 | 0 | 3 | Sage, Miss. Stella Anna | female | NaN | 8 | 2 | CA. 2343 | 69.55 | NaN | S |
| 846 | 847 | 0 | 3 | Sage, Mr. Douglas Bullen | male | NaN | 8 | 2 | CA. 2343 | 69.55 | NaN | S |
| 863 | 864 | 0 | 3 | Sage, Miss. Dorothy Edith "Dolly" | female | NaN | 8 | 2 | CA. 2343 | 69.55 | NaN | S |
As there are no null values in the SibSp column, the last sibling may just have been excluded from this dataset, nothing out of the ordinary.
#Since the number of siblings and spouses are discrete values, we use the countplot to study this variable
labeled_countplot(titantrain,'Parch', perc = True)
Observations:
histogram_boxplot(titantrain,'Fare')
count 891.000000 mean 32.204208 std 49.693429 min 0.000000 25% 7.910400 50% 14.454200 75% 31.000000 max 512.329200 Name: Fare, dtype: float64
#Let's look at the top 10 most expensive tickets on the titanic, and see if 512 is a common price for the tickets
titantrain.sort_values(by = 'Fare', ascending = False).head(10)
| PassengerId | Survived | Pclass | Name | Sex | Age | SibSp | Parch | Ticket | Fare | Cabin | Embarked | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 258 | 259 | 1 | 1 | Ward, Miss. Anna | female | 35.0 | 0 | 0 | PC 17755 | 512.3292 | NaN | C |
| 737 | 738 | 1 | 1 | Lesurer, Mr. Gustave J | male | 35.0 | 0 | 0 | PC 17755 | 512.3292 | B101 | C |
| 679 | 680 | 1 | 1 | Cardeza, Mr. Thomas Drake Martinez | male | 36.0 | 0 | 1 | PC 17755 | 512.3292 | B51 B53 B55 | C |
| 88 | 89 | 1 | 1 | Fortune, Miss. Mabel Helen | female | 23.0 | 3 | 2 | 19950 | 263.0000 | C23 C25 C27 | S |
| 27 | 28 | 0 | 1 | Fortune, Mr. Charles Alexander | male | 19.0 | 3 | 2 | 19950 | 263.0000 | C23 C25 C27 | S |
| 341 | 342 | 1 | 1 | Fortune, Miss. Alice Elizabeth | female | 24.0 | 3 | 2 | 19950 | 263.0000 | C23 C25 C27 | S |
| 438 | 439 | 0 | 1 | Fortune, Mr. Mark | male | 64.0 | 1 | 4 | 19950 | 263.0000 | C23 C25 C27 | S |
| 311 | 312 | 1 | 1 | Ryerson, Miss. Emily Borie | female | 18.0 | 2 | 2 | PC 17608 | 262.3750 | B57 B59 B63 B66 | C |
| 742 | 743 | 1 | 1 | Ryerson, Miss. Susan Parker "Suzette" | female | 21.0 | 2 | 2 | PC 17608 | 262.3750 | B57 B59 B63 B66 | C |
| 118 | 119 | 0 | 1 | Baxter, Mr. Quigg Edmond | male | 24.0 | 0 | 1 | PC 17558 | 247.5208 | B58 B60 | C |
# The cheapest tickets are 0, which means the tickets are free. Let's take a look and see if that is normal
titantrain.loc[titantrain['Fare'] == 0]
| PassengerId | Survived | Pclass | Name | Sex | Age | SibSp | Parch | Ticket | Fare | Cabin | Embarked | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 179 | 180 | 0 | 3 | Leonard, Mr. Lionel | male | 36.0 | 0 | 0 | LINE | 0.0 | NaN | S |
| 263 | 264 | 0 | 1 | Harrison, Mr. William | male | 40.0 | 0 | 0 | 112059 | 0.0 | B94 | S |
| 271 | 272 | 1 | 3 | Tornquist, Mr. William Henry | male | 25.0 | 0 | 0 | LINE | 0.0 | NaN | S |
| 277 | 278 | 0 | 2 | Parkes, Mr. Francis "Frank" | male | NaN | 0 | 0 | 239853 | 0.0 | NaN | S |
| 302 | 303 | 0 | 3 | Johnson, Mr. William Cahoone Jr | male | 19.0 | 0 | 0 | LINE | 0.0 | NaN | S |
| 413 | 414 | 0 | 2 | Cunningham, Mr. Alfred Fleming | male | NaN | 0 | 0 | 239853 | 0.0 | NaN | S |
| 466 | 467 | 0 | 2 | Campbell, Mr. William | male | NaN | 0 | 0 | 239853 | 0.0 | NaN | S |
| 481 | 482 | 0 | 2 | Frost, Mr. Anthony Wood "Archie" | male | NaN | 0 | 0 | 239854 | 0.0 | NaN | S |
| 597 | 598 | 0 | 3 | Johnson, Mr. Alfred | male | 49.0 | 0 | 0 | LINE | 0.0 | NaN | S |
| 633 | 634 | 0 | 1 | Parr, Mr. William Henry Marsh | male | NaN | 0 | 0 | 112052 | 0.0 | NaN | S |
| 674 | 675 | 0 | 2 | Watson, Mr. Ennis Hastings | male | NaN | 0 | 0 | 239856 | 0.0 | NaN | S |
| 732 | 733 | 0 | 2 | Knight, Mr. Robert J | male | NaN | 0 | 0 | 239855 | 0.0 | NaN | S |
| 806 | 807 | 0 | 1 | Andrews, Mr. Thomas Jr | male | 39.0 | 0 | 0 | 112050 | 0.0 | A36 | S |
| 815 | 816 | 0 | 1 | Fry, Mr. Richard | male | NaN | 0 | 0 | 112058 | 0.0 | B102 | S |
| 822 | 823 | 0 | 1 | Reuchlin, Jonkheer. John George | male | 38.0 | 0 | 0 | 19972 | 0.0 | NaN | S |
Observations:
Trivia: Based on research, it seems like Anna Ward and Gustave J Lesurer were both servants to thomas Drake Martinez
labeled_countplot(titantrain,'Survived',True)
Observations:
labeled_countplot(titantrain, 'Pclass',True)
Observations:
labeled_countplot(titantrain, 'Sex',True)
Ticket is akin to Name, another identifier. But let's still look into the data to se if there is any pattern inside.
#Number of unique ticket numbers
titantrain['Ticket'].nunique()
681
#Which are the tickets with multiple counts?
titantrain['Ticket'].value_counts()
Ticket
347082 7
CA. 2343 7
1601 7
3101295 6
CA 2144 6
..
9234 1
19988 1
2693 1
PC 17612 1
370376 1
Name: count, Length: 681, dtype: int64
#Why do these tickets have multiple counts?
titantrain.loc[titantrain['Ticket'] == '347082']
| PassengerId | Survived | Pclass | Name | Sex | Age | SibSp | Parch | Ticket | Fare | Cabin | Embarked | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 13 | 14 | 0 | 3 | Andersson, Mr. Anders Johan | male | 39.0 | 1 | 5 | 347082 | 31.275 | NaN | S |
| 119 | 120 | 0 | 3 | Andersson, Miss. Ellis Anna Maria | female | 2.0 | 4 | 2 | 347082 | 31.275 | NaN | S |
| 541 | 542 | 0 | 3 | Andersson, Miss. Ingeborg Constanzia | female | 9.0 | 4 | 2 | 347082 | 31.275 | NaN | S |
| 542 | 543 | 0 | 3 | Andersson, Miss. Sigrid Elisabeth | female | 11.0 | 4 | 2 | 347082 | 31.275 | NaN | S |
| 610 | 611 | 0 | 3 | Andersson, Mrs. Anders Johan (Alfrida Konstant... | female | 39.0 | 1 | 5 | 347082 | 31.275 | NaN | S |
| 813 | 814 | 0 | 3 | Andersson, Miss. Ebba Iris Alfrida | female | 6.0 | 4 | 2 | 347082 | 31.275 | NaN | S |
| 850 | 851 | 0 | 3 | Andersson, Master. Sigvard Harald Elias | male | 4.0 | 4 | 2 | 347082 | 31.275 | NaN | S |
Observations:
nullcount = round(titantrain['Cabin'].isna().sum()/len(titantrain)*100,2)
print(f'{nullcount}% of data is null!')
77.1% of data is null!
titantrain['Cabin'].value_counts()
Cabin B96 B98 4 G6 4 C23 C25 C27 4 C22 C26 3 F33 3 F2 3 E101 3 D 3 C78 2 C93 2 E8 2 D36 2 B77 2 C123 2 E121 2 E44 2 D35 2 C125 2 E67 2 B35 2 B18 2 E24 2 B49 2 C65 2 B20 2 B5 2 B57 B59 B63 B66 2 C126 2 B51 B53 B55 2 F4 2 C124 2 F G73 2 B58 B60 2 C52 2 D33 2 C68 2 D20 2 D26 2 B28 2 C83 2 E25 2 D17 2 B22 2 C92 2 C2 2 E33 2 C70 1 E58 1 A16 1 C86 1 D19 1 D48 1 A26 1 B50 1 A20 1 C101 1 A10 1 A23 1 E68 1 D9 1 B41 1 D50 1 C85 1 B71 1 D49 1 B42 1 C50 1 A24 1 E17 1 D28 1 C47 1 E49 1 B69 1 B102 1 A36 1 B82 B84 1 D6 1 B3 1 F38 1 E77 1 D11 1 D30 1 C46 1 D45 1 B101 1 B38 1 C45 1 C90 1 C62 C64 1 F G63 1 B39 1 E10 1 C95 1 B86 1 C99 1 B94 1 C87 1 D15 1 A31 1 B80 1 B4 1 A32 1 C49 1 A7 1 B19 1 D47 1 D7 1 F E69 1 C110 1 D10 D12 1 A5 1 E31 1 B30 1 B78 1 A6 1 D56 1 C103 1 E46 1 C118 1 A19 1 B73 1 A34 1 D46 1 B79 1 C30 1 B37 1 A14 1 E63 1 E12 1 D21 1 E38 1 C111 1 C104 1 C82 1 C106 1 E50 1 D37 1 C128 1 T 1 E40 1 C91 1 C32 1 E34 1 C7 1 C54 1 E36 1 C148 1 Name: count, dtype: int64
Observations:
The alphabet indicates the deck layer, and the numbers indicate the location of the cabin on the floor. So we can separate this column into the alphabets and the numbers to further understand if the location of the cabin on the Titanic will influence the survival rate
# Extract alphabets from cabin and categorize them into single alphabets
titantrain['cabin_alphabet'] = titantrain['Cabin'].str.extractall(r'([A-Za-z]+)').groupby(level=0)[0].apply(lambda x: ','.join(x))
titantrain['cabin_alphabet'] = titantrain['cabin_alphabet'].str.split(',').str[0]
#Extract numbers from cabin and obtaining the mean of the passengers with multiple rooms
titantrain['cabin_numbers'] = titantrain['Cabin'].str.extractall(r'(\d+)').groupby(level=0)[0].agg(list).apply(lambda x: sum(map(int, x)) / len(x)).astype(int)
titantrain.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 891 entries, 0 to 890 Data columns (total 14 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 PassengerId 891 non-null object 1 Survived 891 non-null object 2 Pclass 891 non-null object 3 Name 891 non-null object 4 Sex 891 non-null object 5 Age 714 non-null float64 6 SibSp 891 non-null int64 7 Parch 891 non-null int64 8 Ticket 891 non-null object 9 Fare 891 non-null float64 10 Cabin 204 non-null object 11 Embarked 889 non-null object 12 cabin_alphabet 204 non-null object 13 cabin_numbers 200 non-null float64 dtypes: float64(3), int64(2), object(9) memory usage: 97.6+ KB
titantrain['cabin_alphabet'].value_counts()
cabin_alphabet C 59 B 47 D 33 E 32 A 15 F 13 G 4 T 1 Name: count, dtype: int64
Cabin Alphabets
labeled_countplot(titantrain, 'cabin_alphabet', order = ['A','B','C','D','E','F','G','T'])
Observations:
Cabin Numbers
histogram_boxplot(titantrain, 'cabin_numbers')
count 200.000000 mean 50.665000 std 35.380527 min 2.000000 25% 24.000000 50% 43.000000 75% 77.250000 max 148.000000 Name: cabin_numbers, dtype: float64
g = sns.histplot(data = titantrain, x = 'cabin_numbers', hue = 'Survived', kde = True, palette = 'bright')
g.xaxis.set_major_locator(ticker.MultipleLocator(25))
Observations:
labeled_countplot(titantrain, 'Embarked',True)
Observations:
titantrain['Survived'] = titantrain['Survived'].astype('int')
plt.figure(figsize = (8,6))
sns.heatmap(titantrain.corr(numeric_only = True), annot = True, cmap = 'viridis')
<Axes: >
Observations:
sns.countplot(x = titantrain['cabin_alphabet'], hue = titantrain['Survived'], order = ['A','B','C','D','E','F','G','T'])
<Axes: xlabel='cabin_alphabet', ylabel='count'>
Observations:
sns.countplot(x = titantrain['Sex'], hue = titantrain['Survived'])
<Axes: xlabel='Sex', ylabel='count'>
Observations:
sns.kdeplot(titantrain, x = 'Age', hue = 'Survived', shade = True)
<Axes: xlabel='Age', ylabel='Density'>
Observations:
sns.swarmplot(titantrain, x ='Sex', y = 'Age', hue = 'Survived')
<Axes: xlabel='Sex', ylabel='Age'>
Observations:
stacked_barplot(titantrain,'Pclass','Survived')
Survived 0 1 All Pclass All 549 342 891 1 80 136 216 3 372 119 491 2 97 87 184 ------------------------------------------------------------------------------------------------------------------------
Observations:
sns.set_style("whitegrid")
sns.swarmplot(titantrain, x = 'Pclass', y = 'Age', hue = 'Survived')
<Axes: xlabel='Pclass', ylabel='Age'>
titantrain.loc[(titantrain['Survived'] == 1) & (titantrain['Pclass'] == 1)].sort_values(by='Age',ascending = True)
| PassengerId | Survived | Pclass | Name | Sex | Age | SibSp | Parch | Ticket | Fare | Cabin | Embarked | cabin_alphabet | cabin_numbers | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 305 | 306 | 1 | 1 | Allison, Master. Hudson Trevor | male | 0.92 | 1 | 2 | 113781 | 151.5500 | C22 C26 | S | C | 24.0 |
| 445 | 446 | 1 | 1 | Dodge, Master. Washington | male | 4.00 | 0 | 2 | 33638 | 81.8583 | A34 | S | A | 34.0 |
| 802 | 803 | 1 | 1 | Carter, Master. William Thornton II | male | 11.00 | 1 | 2 | 113760 | 120.0000 | B96 B98 | S | B | 97.0 |
| 435 | 436 | 1 | 1 | Carter, Miss. Lucile Polk | female | 14.00 | 1 | 2 | 113760 | 120.0000 | B96 B98 | S | B | 97.0 |
| 689 | 690 | 1 | 1 | Madill, Miss. Georgette Alexandra | female | 15.00 | 0 | 1 | 24160 | 211.3375 | B5 | S | B | 5.0 |
| 504 | 505 | 1 | 1 | Maioni, Miss. Roberta | female | 16.00 | 0 | 0 | 110152 | 86.5000 | B79 | S | B | 79.0 |
| 853 | 854 | 1 | 1 | Lines, Miss. Mary Conover | female | 16.00 | 0 | 1 | PC 17592 | 39.4000 | D28 | S | D | 28.0 |
| 329 | 330 | 1 | 1 | Hippach, Miss. Jean Gertrude | female | 16.00 | 0 | 1 | 111361 | 57.9792 | B18 | C | B | 18.0 |
| 307 | 308 | 1 | 1 | Penasco y Castellana, Mrs. Victor de Satode (M... | female | 17.00 | 1 | 0 | PC 17758 | 108.9000 | C65 | C | C | 65.0 |
| 550 | 551 | 1 | 1 | Thayer, Mr. John Borland Jr | male | 17.00 | 0 | 2 | 17421 | 110.8833 | C70 | C | C | 70.0 |
| 781 | 782 | 1 | 1 | Dick, Mrs. Albert Adrian (Vera Gillespie) | female | 17.00 | 1 | 0 | 17474 | 57.0000 | B20 | S | B | 20.0 |
| 311 | 312 | 1 | 1 | Ryerson, Miss. Emily Borie | female | 18.00 | 2 | 2 | PC 17608 | 262.3750 | B57 B59 B63 B66 | C | B | 61.0 |
| 700 | 701 | 1 | 1 | Astor, Mrs. John Jacob (Madeleine Talmadge Force) | female | 18.00 | 1 | 0 | PC 17757 | 227.5250 | C62 C64 | C | C | 63.0 |
| 585 | 586 | 1 | 1 | Taussig, Miss. Ruth | female | 18.00 | 0 | 2 | 110413 | 79.6500 | E68 | S | E | 68.0 |
| 887 | 888 | 1 | 1 | Graham, Miss. Margaret Edith | female | 19.00 | 0 | 0 | 112053 | 30.0000 | B42 | S | B | 42.0 |
| 136 | 137 | 1 | 1 | Newsom, Miss. Helen Monypeny | female | 19.00 | 0 | 2 | 11752 | 26.2833 | D47 | S | D | 47.0 |
| 291 | 292 | 1 | 1 | Bishop, Mrs. Dickinson H (Helen Walton) | female | 19.00 | 1 | 0 | 11967 | 91.0792 | B49 | C | B | 49.0 |
| 742 | 743 | 1 | 1 | Ryerson, Miss. Susan Parker "Suzette" | female | 21.00 | 2 | 2 | PC 17608 | 262.3750 | B57 B59 B63 B66 | C | B | 61.0 |
| 627 | 628 | 1 | 1 | Longley, Miss. Gretchen Fiske | female | 21.00 | 0 | 0 | 13502 | 77.9583 | D9 | S | D | 9.0 |
| 539 | 540 | 1 | 1 | Frolicher, Miss. Hedwig Margaritha | female | 22.00 | 0 | 2 | 13568 | 49.5000 | B39 | C | B | 39.0 |
| 708 | 709 | 1 | 1 | Cleaver, Miss. Alice | female | 22.00 | 0 | 0 | 113781 | 151.5500 | NaN | S | NaN | NaN |
| 356 | 357 | 1 | 1 | Bowerman, Miss. Elsie Edith | female | 22.00 | 0 | 1 | 113505 | 55.0000 | E33 | S | E | 33.0 |
| 151 | 152 | 1 | 1 | Pears, Mrs. Thomas (Edith Wearne) | female | 22.00 | 1 | 0 | 113776 | 66.6000 | C2 | S | C | 2.0 |
| 393 | 394 | 1 | 1 | Newell, Miss. Marjorie | female | 23.00 | 1 | 0 | 35273 | 113.2750 | D36 | C | D | 36.0 |
| 88 | 89 | 1 | 1 | Fortune, Miss. Mabel Helen | female | 23.00 | 3 | 2 | 19950 | 263.0000 | C23 C25 C27 | S | C | 25.0 |
| 97 | 98 | 1 | 1 | Greenfield, Mr. William Bertram | male | 23.00 | 0 | 1 | PC 17759 | 63.3583 | D10 D12 | C | D | 11.0 |
| 369 | 370 | 1 | 1 | Aubart, Mme. Leontine Pauline | female | 24.00 | 0 | 0 | PC 17477 | 69.3000 | B35 | C | B | 35.0 |
| 341 | 342 | 1 | 1 | Fortune, Miss. Alice Elizabeth | female | 24.00 | 3 | 2 | 19950 | 263.0000 | C23 C25 C27 | S | C | 25.0 |
| 641 | 642 | 1 | 1 | Sagesser, Mlle. Emma | female | 24.00 | 0 | 0 | PC 17477 | 69.3000 | B35 | C | B | 35.0 |
| 710 | 711 | 1 | 1 | Mayne, Mlle. Berthe Antonine ("Mrs de Villiers") | female | 24.00 | 0 | 0 | PC 17482 | 49.5042 | C90 | C | C | 90.0 |
| 310 | 311 | 1 | 1 | Hays, Miss. Margaret Bechstein | female | 24.00 | 0 | 0 | 11767 | 83.1583 | C54 | C | C | 54.0 |
| 484 | 485 | 1 | 1 | Bishop, Mr. Dickinson H | male | 25.00 | 1 | 0 | 11967 | 91.0792 | B49 | C | B | 49.0 |
| 370 | 371 | 1 | 1 | Harder, Mr. George Achilles | male | 25.00 | 1 | 0 | 11765 | 55.4417 | E50 | C | E | 50.0 |
| 290 | 291 | 1 | 1 | Barber, Miss. Ellen "Nellie" | female | 26.00 | 0 | 0 | 19877 | 78.8500 | NaN | S | NaN | NaN |
| 889 | 890 | 1 | 1 | Behr, Mr. Karl Howell | male | 26.00 | 0 | 0 | 111369 | 30.0000 | C148 | C | C | 148.0 |
| 724 | 725 | 1 | 1 | Chambers, Mr. Norman Campbell | male | 27.00 | 1 | 0 | 113806 | 53.1000 | E8 | S | E | 8.0 |
| 681 | 682 | 1 | 1 | Hassab, Mr. Hammad | male | 27.00 | 0 | 0 | PC 17572 | 76.7292 | D49 | C | D | 49.0 |
| 607 | 608 | 1 | 1 | Daniel, Mr. Robert Williams | male | 27.00 | 0 | 0 | 113804 | 30.5000 | NaN | S | NaN | NaN |
| 23 | 24 | 1 | 1 | Sloper, Mr. William Thompson | male | 28.00 | 0 | 0 | 113788 | 35.5000 | A6 | S | A | 6.0 |
| 430 | 431 | 1 | 1 | Bjornstrom-Steffansson, Mr. Mauritz Hakan | male | 28.00 | 0 | 0 | 110564 | 26.5500 | C52 | S | C | 52.0 |
| 730 | 731 | 1 | 1 | Allen, Miss. Elisabeth Walton | female | 29.00 | 0 | 0 | 24160 | 211.3375 | B5 | S | B | 5.0 |
| 537 | 538 | 1 | 1 | LeRoy, Miss. Bertha | female | 30.00 | 0 | 0 | PC 17761 | 106.4250 | NaN | C | NaN | NaN |
| 842 | 843 | 1 | 1 | Serepeca, Miss. Augusta | female | 30.00 | 0 | 0 | 113798 | 31.0000 | NaN | C | NaN | NaN |
| 520 | 521 | 1 | 1 | Perreault, Miss. Anne | female | 30.00 | 0 | 0 | 12749 | 93.5000 | B73 | S | B | 73.0 |
| 257 | 258 | 1 | 1 | Cherry, Miss. Gladys | female | 30.00 | 0 | 0 | 110152 | 86.5000 | B77 | S | B | 77.0 |
| 309 | 310 | 1 | 1 | Francatelli, Miss. Laura Mabel | female | 30.00 | 0 | 0 | PC 17485 | 56.9292 | E36 | C | E | 36.0 |
| 215 | 216 | 1 | 1 | Newell, Miss. Madeleine | female | 31.00 | 1 | 0 | 35273 | 113.2750 | D36 | C | D | 36.0 |
| 690 | 691 | 1 | 1 | Dick, Mr. Albert Adrian | male | 31.00 | 1 | 0 | 17474 | 57.0000 | B20 | S | B | 20.0 |
| 318 | 319 | 1 | 1 | Wick, Miss. Mary Natalie | female | 31.00 | 0 | 2 | 36928 | 164.8667 | C7 | S | C | 7.0 |
| 632 | 633 | 1 | 1 | Stahelin-Maeglin, Dr. Max | male | 32.00 | 0 | 0 | 13214 | 30.5000 | B50 | C | B | 50.0 |
| 218 | 219 | 1 | 1 | Bazzani, Miss. Albina | female | 32.00 | 0 | 0 | 11813 | 76.2917 | D15 | C | D | 15.0 |
| 412 | 413 | 1 | 1 | Minahan, Miss. Daisy E | female | 33.00 | 1 | 0 | 19928 | 90.0000 | C78 | Q | C | 78.0 |
| 809 | 810 | 1 | 1 | Chambers, Mrs. Norman Campbell (Bertha Griggs) | female | 33.00 | 1 | 0 | 113806 | 53.1000 | E8 | S | E | 8.0 |
| 759 | 760 | 1 | 1 | Rothes, the Countess. of (Lucy Noel Martha Dye... | female | 33.00 | 0 | 0 | 110152 | 86.5000 | B77 | S | B | 77.0 |
| 447 | 448 | 1 | 1 | Seward, Mr. Frederic Kimber | male | 34.00 | 0 | 0 | 113794 | 26.5500 | NaN | S | NaN | NaN |
| 604 | 605 | 1 | 1 | Homer, Mr. Harry ("Mr E Haven") | male | 35.00 | 0 | 0 | 111426 | 26.5500 | NaN | C | NaN | NaN |
| 258 | 259 | 1 | 1 | Ward, Miss. Anna | female | 35.00 | 0 | 0 | PC 17755 | 512.3292 | NaN | C | NaN | NaN |
| 383 | 384 | 1 | 1 | Holverson, Mrs. Alexander Oskar (Mary Aline To... | female | 35.00 | 1 | 0 | 113789 | 52.0000 | NaN | S | NaN | NaN |
| 486 | 487 | 1 | 1 | Hoyt, Mrs. Frederick Maxfield (Jane Anne Forby) | female | 35.00 | 1 | 0 | 19943 | 90.0000 | C93 | S | C | 93.0 |
| 269 | 270 | 1 | 1 | Bissette, Miss. Amelia | female | 35.00 | 0 | 0 | PC 17760 | 135.6333 | C99 | S | C | 99.0 |
| 701 | 702 | 1 | 1 | Silverthorne, Mr. Spencer Victor | male | 35.00 | 0 | 0 | PC 17475 | 26.2875 | E24 | S | E | 24.0 |
| 737 | 738 | 1 | 1 | Lesurer, Mr. Gustave J | male | 35.00 | 0 | 0 | PC 17755 | 512.3292 | B101 | C | B | 101.0 |
| 3 | 4 | 1 | 1 | Futrelle, Mrs. Jacques Heath (Lily May Peel) | female | 35.00 | 1 | 0 | 113803 | 53.1000 | C123 | S | C | 123.0 |
| 230 | 231 | 1 | 1 | Harris, Mrs. Henry Birkhardt (Irene Wallach) | female | 35.00 | 1 | 0 | 36973 | 83.4750 | C83 | S | C | 83.0 |
| 679 | 680 | 1 | 1 | Cardeza, Mr. Thomas Drake Martinez | male | 36.00 | 0 | 1 | PC 17755 | 512.3292 | B51 B53 B55 | C | B | 53.0 |
| 763 | 764 | 1 | 1 | Carter, Mrs. William Ernest (Lucile Polk) | female | 36.00 | 1 | 2 | 113760 | 120.0000 | B96 B98 | S | B | 97.0 |
| 572 | 573 | 1 | 1 | Flynn, Mr. John Irwin ("Irving") | male | 36.00 | 0 | 0 | PC 17474 | 26.3875 | E25 | S | E | 25.0 |
| 390 | 391 | 1 | 1 | Carter, Mr. William Ernest | male | 36.00 | 1 | 2 | 113760 | 120.0000 | B96 B98 | S | B | 97.0 |
| 325 | 326 | 1 | 1 | Young, Miss. Marie Grice | female | 36.00 | 0 | 0 | PC 17760 | 135.6333 | C32 | C | C | 32.0 |
| 512 | 513 | 1 | 1 | McGough, Mr. James Robert | male | 36.00 | 0 | 0 | PC 17473 | 26.2875 | E25 | S | E | 25.0 |
| 540 | 541 | 1 | 1 | Crosby, Miss. Harriet R | female | 36.00 | 0 | 2 | WE/P 5735 | 71.0000 | B22 | S | B | 22.0 |
| 248 | 249 | 1 | 1 | Beckwith, Mr. Richard Leonard | male | 37.00 | 1 | 1 | 11751 | 52.5542 | D35 | S | D | 35.0 |
| 716 | 717 | 1 | 1 | Endres, Miss. Caroline Louise | female | 38.00 | 0 | 0 | PC 17757 | 227.5250 | C45 | C | C | 45.0 |
| 224 | 225 | 1 | 1 | Hoyt, Mr. Frederick Maxfield | male | 38.00 | 1 | 0 | 19943 | 90.0000 | C93 | S | C | 93.0 |
| 61 | 62 | 1 | 1 | Icard, Miss. Amelie | female | 38.00 | 0 | 0 | 113572 | 80.0000 | B28 | NaN | B | 28.0 |
| 1 | 2 | 1 | 1 | Cumings, Mrs. John Bradley (Florence Briggs Th... | female | 38.00 | 1 | 0 | PC 17599 | 71.2833 | C85 | C | C | 85.0 |
| 835 | 836 | 1 | 1 | Compton, Miss. Sara Rebecca | female | 39.00 | 1 | 1 | PC 17756 | 83.1583 | E49 | C | E | 49.0 |
| 558 | 559 | 1 | 1 | Taussig, Mrs. Emil (Tillie Mandelbaum) | female | 39.00 | 1 | 1 | 110413 | 79.6500 | E67 | S | E | 67.0 |
| 577 | 578 | 1 | 1 | Silvey, Mrs. William Baird (Alice Munger) | female | 39.00 | 1 | 0 | 13507 | 55.9000 | E44 | S | E | 44.0 |
| 581 | 582 | 1 | 1 | Thayer, Mrs. John Borland (Marian Longstreth M... | female | 39.00 | 1 | 1 | 17421 | 110.8833 | C68 | C | C | 68.0 |
| 609 | 610 | 1 | 1 | Shutes, Miss. Elizabeth W | female | 40.00 | 0 | 0 | PC 17582 | 153.4625 | C125 | S | C | 125.0 |
| 209 | 210 | 1 | 1 | Blank, Mr. Henry | male | 40.00 | 0 | 0 | 112277 | 31.0000 | A31 | C | A | 31.0 |
| 319 | 320 | 1 | 1 | Spedden, Mrs. Frederic Oakley (Margaretta Corn... | female | 40.00 | 1 | 1 | 16966 | 134.5000 | E34 | C | E | 34.0 |
| 337 | 338 | 1 | 1 | Burns, Miss. Elizabeth Margaret | female | 41.00 | 0 | 0 | 16966 | 134.5000 | E40 | C | E | 40.0 |
| 621 | 622 | 1 | 1 | Kimball, Mr. Edwin Nelson Jr | male | 42.00 | 1 | 0 | 11753 | 52.5542 | D19 | S | D | 19.0 |
| 707 | 708 | 1 | 1 | Calderhead, Mr. Edward Pennington | male | 42.00 | 0 | 0 | PC 17476 | 26.2875 | E24 | S | E | 24.0 |
| 380 | 381 | 1 | 1 | Bidois, Miss. Rosalie | female | 42.00 | 0 | 0 | PC 17757 | 227.5250 | NaN | C | NaN | NaN |
| 779 | 780 | 1 | 1 | Robert, Mrs. Edward Scott (Elisabeth Walton Mc... | female | 43.00 | 0 | 1 | 24160 | 211.3375 | B3 | S | B | 3.0 |
| 194 | 195 | 1 | 1 | Brown, Mrs. James Joseph (Margaret Tobin) | female | 44.00 | 0 | 0 | PC 17610 | 27.7208 | B4 | C | B | 4.0 |
| 523 | 524 | 1 | 1 | Hippach, Mrs. Louis Albert (Ida Sophia Fischer) | female | 44.00 | 0 | 1 | 111361 | 57.9792 | B18 | C | B | 18.0 |
| 187 | 188 | 1 | 1 | Romaine, Mr. Charles Hallace ("Mr C Rolmane") | male | 45.00 | 0 | 0 | 111428 | 26.5500 | NaN | S | NaN | NaN |
| 856 | 857 | 1 | 1 | Wick, Mrs. George Dennick (Mary Hitchcock) | female | 45.00 | 1 | 1 | 36928 | 164.8667 | NaN | S | NaN | NaN |
| 871 | 872 | 1 | 1 | Beckwith, Mrs. Richard Leonard (Sallie Monypeny) | female | 47.00 | 1 | 1 | 11751 | 52.5542 | D35 | S | D | 35.0 |
| 862 | 863 | 1 | 1 | Swift, Mrs. Frederick Joel (Margaret Welles Ba... | female | 48.00 | 0 | 0 | 17466 | 25.9292 | D17 | S | D | 17.0 |
| 645 | 646 | 1 | 1 | Harper, Mr. Henry Sleeper | male | 48.00 | 1 | 0 | PC 17572 | 76.7292 | D33 | C | D | 33.0 |
| 460 | 461 | 1 | 1 | Anderson, Mr. Harry | male | 48.00 | 0 | 0 | 19952 | 26.5500 | E12 | S | E | 12.0 |
| 712 | 713 | 1 | 1 | Taylor, Mr. Elmer Zebley | male | 48.00 | 1 | 0 | 19996 | 52.0000 | C126 | S | C | 126.0 |
| 556 | 557 | 1 | 1 | Duff Gordon, Lady. (Lucille Christiana Sutherl... | female | 48.00 | 1 | 0 | 11755 | 39.6000 | A16 | C | A | 16.0 |
| 796 | 797 | 1 | 1 | Leader, Dr. Alice (Farnham) | female | 49.00 | 0 | 0 | 17465 | 25.9292 | D17 | S | D | 17.0 |
| 52 | 53 | 1 | 1 | Harper, Mrs. Henry Sleeper (Myna Haxtun) | female | 49.00 | 1 | 0 | PC 17572 | 76.7292 | D33 | C | D | 33.0 |
| 453 | 454 | 1 | 1 | Goldenberg, Mr. Samuel L | male | 49.00 | 1 | 0 | 17453 | 89.1042 | C92 | C | C | 92.0 |
| 599 | 600 | 1 | 1 | Duff Gordon, Sir. Cosmo Edmund ("Mr Morgan") | male | 49.00 | 1 | 0 | PC 17485 | 56.9292 | A20 | C | A | 20.0 |
| 660 | 661 | 1 | 1 | Frauenthal, Dr. Henry William | male | 50.00 | 2 | 0 | PC 17611 | 133.6500 | NaN | S | NaN | NaN |
| 299 | 300 | 1 | 1 | Baxter, Mrs. James (Helene DeLaudeniere Chaput) | female | 50.00 | 0 | 1 | PC 17558 | 247.5208 | B58 B60 | C | B | 59.0 |
| 765 | 766 | 1 | 1 | Hogeboom, Mrs. John C (Anna Andrews) | female | 51.00 | 1 | 0 | 13502 | 77.9583 | D11 | S | D | 11.0 |
| 857 | 858 | 1 | 1 | Daly, Mr. Peter Denis | male | 51.00 | 0 | 0 | 113055 | 26.5500 | E17 | S | E | 17.0 |
| 820 | 821 | 1 | 1 | Hays, Mrs. Charles Melville (Clara Jennings Gr... | female | 52.00 | 1 | 1 | 12749 | 93.5000 | B69 | S | B | 69.0 |
| 449 | 450 | 1 | 1 | Peuchen, Major. Arthur Godfrey | male | 52.00 | 0 | 0 | 113786 | 30.5000 | C104 | S | C | 104.0 |
| 591 | 592 | 1 | 1 | Stephenson, Mrs. Walter Bertram (Martha Eustis) | female | 52.00 | 1 | 0 | 36947 | 78.2667 | D20 | C | D | 20.0 |
| 571 | 572 | 1 | 1 | Appleton, Mrs. Edward Dale (Charlotte Lamson) | female | 53.00 | 2 | 0 | 11769 | 51.4792 | C101 | S | C | 101.0 |
| 496 | 497 | 1 | 1 | Eustis, Miss. Elizabeth Mussey | female | 54.00 | 1 | 0 | 36947 | 78.2667 | D20 | C | D | 20.0 |
| 513 | 514 | 1 | 1 | Rothschild, Mrs. Martin (Elizabeth L. Barrett) | female | 54.00 | 1 | 0 | PC 17603 | 59.4000 | NaN | C | NaN | NaN |
| 647 | 648 | 1 | 1 | Simonius-Blumer, Col. Oberst Alfons | male | 56.00 | 0 | 0 | 13213 | 35.5000 | A26 | C | A | 26.0 |
| 879 | 880 | 1 | 1 | Potter, Mrs. Thomas Jr (Lily Alexenia Wilson) | female | 56.00 | 0 | 1 | 11767 | 83.1583 | C50 | C | C | 50.0 |
| 268 | 269 | 1 | 1 | Graham, Mrs. William Thompson (Edith Junkins) | female | 58.00 | 0 | 1 | PC 17582 | 153.4625 | C125 | S | C | 125.0 |
| 195 | 196 | 1 | 1 | Lurette, Miss. Elise | female | 58.00 | 0 | 0 | PC 17569 | 146.5208 | B80 | C | B | 80.0 |
| 11 | 12 | 1 | 1 | Bonnell, Miss. Elizabeth | female | 58.00 | 0 | 0 | 113783 | 26.5500 | C103 | S | C | 103.0 |
| 587 | 588 | 1 | 1 | Frolicher-Stehli, Mr. Maxmillian | male | 60.00 | 1 | 1 | 13567 | 79.2000 | B41 | C | B | 41.0 |
| 366 | 367 | 1 | 1 | Warren, Mrs. Frank Manley (Anna Sophia Atkinson) | female | 60.00 | 1 | 0 | 110813 | 75.2500 | D37 | C | D | 37.0 |
| 829 | 830 | 1 | 1 | Stone, Mrs. George Nelson (Martha Evelyn) | female | 62.00 | 0 | 0 | 113572 | 80.0000 | B28 | NaN | B | 28.0 |
| 275 | 276 | 1 | 1 | Andrews, Miss. Kornelia Theodosia | female | 63.00 | 1 | 0 | 13502 | 77.9583 | D7 | S | D | 7.0 |
| 630 | 631 | 1 | 1 | Barkworth, Mr. Algernon Henry Wilson | male | 80.00 | 0 | 0 | 27042 | 30.0000 | A23 | S | A | 23.0 |
| 31 | 32 | 1 | 1 | Spencer, Mrs. William Augustus (Marie Eugenie) | female | NaN | 1 | 0 | PC 17569 | 146.5208 | B78 | C | B | 78.0 |
| 55 | 56 | 1 | 1 | Woolner, Mr. Hugh | male | NaN | 0 | 0 | 19947 | 35.5000 | C52 | S | C | 52.0 |
| 166 | 167 | 1 | 1 | Chibnall, Mrs. (Edith Martha Bowerman) | female | NaN | 0 | 1 | 113505 | 55.0000 | E33 | S | E | 33.0 |
| 256 | 257 | 1 | 1 | Thorne, Mrs. Gertrude Maybelle | female | NaN | 0 | 0 | PC 17585 | 79.2000 | NaN | C | NaN | NaN |
| 298 | 299 | 1 | 1 | Saalfeld, Mr. Adolphe | male | NaN | 0 | 0 | 19988 | 30.5000 | C106 | S | C | 106.0 |
| 306 | 307 | 1 | 1 | Fleming, Miss. Margaret | female | NaN | 0 | 0 | 17421 | 110.8833 | NaN | C | NaN | NaN |
| 334 | 335 | 1 | 1 | Frauenthal, Mrs. Henry William (Clara Heinshei... | female | NaN | 1 | 0 | PC 17611 | 133.6500 | NaN | S | NaN | NaN |
| 375 | 376 | 1 | 1 | Meyer, Mrs. Edgar Joseph (Leila Saks) | female | NaN | 1 | 0 | PC 17604 | 82.1708 | NaN | C | NaN | NaN |
| 457 | 458 | 1 | 1 | Kenyon, Mrs. Frederick R (Marion) | female | NaN | 1 | 0 | 17464 | 51.8625 | D21 | S | D | 21.0 |
| 507 | 508 | 1 | 1 | Bradley, Mr. George ("George Arthur Brayton") | male | NaN | 0 | 0 | 111427 | 26.5500 | NaN | S | NaN | NaN |
| 669 | 670 | 1 | 1 | Taylor, Mrs. Elmer Zebley (Juliet Cummins Wright) | female | NaN | 1 | 0 | 19996 | 52.0000 | C126 | S | C | 126.0 |
| 740 | 741 | 1 | 1 | Hawksford, Mr. Walter James | male | NaN | 0 | 0 | 16988 | 30.0000 | D45 | S | D | 45.0 |
| 839 | 840 | 1 | 1 | Marechal, Mr. Pierre | male | NaN | 0 | 0 | 11774 | 29.7000 | C47 | C | C | 47.0 |
| 849 | 850 | 1 | 1 | Goldenberg, Mrs. Samuel L (Edwiga Grabowska) | female | NaN | 1 | 0 | 17453 | 89.1042 | C92 | C | C | 92.0 |
Observations:
plt.figure(figsize = (20,7))
sns.swarmplot(titantrain, x='Sex', y='Fare', hue = 'Survived');
Observations:
stacked_barplot(titantrain,'cabin_alphabet','Embarked')
Embarked C Q S All cabin_alphabet All 69 4 129 202 C 21 2 36 59 E 5 1 26 32 F 1 1 11 13 A 7 0 8 15 B 22 0 23 45 D 13 0 20 33 G 0 0 4 4 T 0 0 1 1 ------------------------------------------------------------------------------------------------------------------------
stacked_barplot(titantrain,'cabin_alphabet','Pclass')
Pclass 1 2 3 All cabin_alphabet All 176 16 12 204 F 0 8 5 13 D 29 4 0 33 E 25 4 3 32 A 15 0 0 15 B 47 0 0 47 C 59 0 0 59 G 0 0 4 4 T 1 0 0 1 ------------------------------------------------------------------------------------------------------------------------
stacked_barplot(titantrain,'cabin_alphabet','Survived')
Survived 0 1 All cabin_alphabet All 68 136 204 B 12 35 47 C 24 35 59 D 8 25 33 E 8 24 32 F 5 8 13 A 8 7 15 G 2 2 4 T 1 0 1 ------------------------------------------------------------------------------------------------------------------------
Observations:
Using Fare to estimate cabin
sns.jointplot(data = titantrain, x = 'cabin_numbers', y = 'Fare',hue = 'Survived')
<seaborn.axisgrid.JointGrid at 0x7eaec5b6e8f0>
plt.figure(figsize = (15,7))
sns.swarmplot(data = titantrain, x = 'cabin_alphabet', y = 'Fare', hue = 'Survived')
<Axes: xlabel='cabin_alphabet', ylabel='Fare'>
Conclusion:
Since the idea of having children, spouse, parents or child are quite similar, we can combine these features into one feature where we count the size of the family.
titantrain['family'] = titantrain['SibSp'] + titantrain['Parch'] + 1
titantrain['family'].value_counts()
family 1 537 2 161 3 102 4 29 6 22 5 15 7 12 11 7 8 6 Name: count, dtype: int64
wait there are some crazy high numbers, lets take a look at what they are
titantrain.loc[titantrain['family']> 8]
| PassengerId | Survived | Pclass | Name | Sex | Age | SibSp | Parch | Ticket | Fare | Cabin | Embarked | cabin_alphabet | cabin_numbers | family | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 159 | 160 | 0 | 3 | Sage, Master. Thomas Henry | male | NaN | 8 | 2 | CA. 2343 | 69.55 | NaN | S | NaN | NaN | 11 |
| 180 | 181 | 0 | 3 | Sage, Miss. Constance Gladys | female | NaN | 8 | 2 | CA. 2343 | 69.55 | NaN | S | NaN | NaN | 11 |
| 201 | 202 | 0 | 3 | Sage, Mr. Frederick | male | NaN | 8 | 2 | CA. 2343 | 69.55 | NaN | S | NaN | NaN | 11 |
| 324 | 325 | 0 | 3 | Sage, Mr. George John Jr | male | NaN | 8 | 2 | CA. 2343 | 69.55 | NaN | S | NaN | NaN | 11 |
| 792 | 793 | 0 | 3 | Sage, Miss. Stella Anna | female | NaN | 8 | 2 | CA. 2343 | 69.55 | NaN | S | NaN | NaN | 11 |
| 846 | 847 | 0 | 3 | Sage, Mr. Douglas Bullen | male | NaN | 8 | 2 | CA. 2343 | 69.55 | NaN | S | NaN | NaN | 11 |
| 863 | 864 | 0 | 3 | Sage, Miss. Dorothy Edith "Dolly" | female | NaN | 8 | 2 | CA. 2343 | 69.55 | NaN | S | NaN | NaN | 11 |
Okay, they are just one big family, with 2 parents and 8 siblings. Nothing out of ordinary
labeled_countplot(titantrain,'family')
sns.countplot(data = titantrain, x = 'family', hue = 'Survived')
<Axes: xlabel='family', ylabel='count'>
Observations:
Let's break down the tickets into ticket number, alphabets and length to understand more about the ticketing system
# Extract alphabets from Ticket and categorize them into single alphabets
titantrain['ticket_alphabet'] = titantrain['Ticket'].str.extractall(r'([A-Za-z]+)').groupby(level=0)[0].apply(lambda x: ','.join(x))
titantrain['ticket_alphabet'] = titantrain['ticket_alphabet'].str.split(',').str[0]
#Extract numbers from Ticket and obtaining the ticket number of the passengers with multiple rooms
titantrain['ticket_numbers'] = titantrain['Ticket'].str.extractall(r'(\d+)').groupby(level=0).agg(lambda x: x.iloc[-1]) #Some ticket numbers have multiple segments of numerical values due to the alphabet portion. So we take only the last segment.
titantrain['ticket_numbers'] = titantrain['ticket_numbers'].apply(lambda x: int(x) if pd.notnull(x) else 0)
#Extract the length of the ticket number
titantrain['ticket_length'] = titantrain['ticket_numbers'].apply(lambda x:len(str(x)) if pd.notnull(x) else 0)
#Seeing what the alphabets look like
titantrain['ticket_alphabet'].value_counts()
ticket_alphabet PC 60 C 33 A 29 STON 18 SOTON 17 S 14 CA 14 SC 13 W 11 F 6 LINE 4 PP 3 P 2 WE 2 SO 1 Fa 1 SW 1 SCO 1 Name: count, dtype: int64
#Seeing if the alphabets is related to the source of embarkation
titantrain.groupby(['Embarked'])['ticket_alphabet'].value_counts()
Embarked ticket_alphabet
C PC 46
SC 11
P 2
S 2
Q A 1
S C 33
A 28
STON 18
SOTON 17
PC 14
CA 14
S 12
W 11
F 6
LINE 4
PP 3
SC 2
WE 2
SCO 1
SO 1
Fa 1
SW 1
Name: count, dtype: int64
Observations:
titantrain['ticket_numbers'].value_counts().sort_index(ascending = False)
ticket_numbers
3101317 1
3101316 1
3101312 1
3101311 1
3101310 1
..
695 1
693 1
541 1
3 2
0 4
Name: count, Length: 679, dtype: int64
sns.histplot(data = titantrain, x = 'ticket_numbers', hue = 'Survived')
<Axes: xlabel='ticket_numbers', ylabel='Count'>
Observations:
titantrain.sort_values(by = 'ticket_length')
| PassengerId | Survived | Pclass | Name | Sex | Age | SibSp | Parch | Ticket | Fare | Cabin | Embarked | cabin_alphabet | cabin_numbers | family | ticket_alphabet | ticket_numbers | ticket_length | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 597 | 598 | 0 | 3 | Johnson, Mr. Alfred | male | 49.0 | 0 | 0 | LINE | 0.0000 | NaN | S | NaN | NaN | 1 | LINE | 0 | 1 |
| 772 | 773 | 0 | 2 | Mack, Mrs. (Mary) | female | 57.0 | 0 | 0 | S.O./P.P. 3 | 10.5000 | E77 | S | E | 77.0 | 1 | S | 3 | 1 |
| 271 | 272 | 1 | 3 | Tornquist, Mr. William Henry | male | 25.0 | 0 | 0 | LINE | 0.0000 | NaN | S | NaN | NaN | 1 | LINE | 0 | 1 |
| 179 | 180 | 0 | 3 | Leonard, Mr. Lionel | male | 36.0 | 0 | 0 | LINE | 0.0000 | NaN | S | NaN | NaN | 1 | LINE | 0 | 1 |
| 302 | 303 | 0 | 3 | Johnson, Mr. William Cahoone Jr | male | 19.0 | 0 | 0 | LINE | 0.0000 | NaN | S | NaN | NaN | 1 | LINE | 0 | 1 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 142 | 143 | 1 | 3 | Hakkarainen, Mrs. Pekka Pietari (Elin Matilda ... | female | 24.0 | 1 | 0 | STON/O2. 3101279 | 15.8500 | NaN | S | NaN | NaN | 2 | STON | 3101279 | 7 |
| 590 | 591 | 0 | 3 | Rintamaki, Mr. Matti | male | 35.0 | 0 | 0 | STON/O 2. 3101273 | 7.1250 | NaN | S | NaN | NaN | 1 | STON | 3101273 | 7 |
| 371 | 372 | 0 | 3 | Wiklund, Mr. Jakob Alfred | male | 18.0 | 1 | 0 | 3101267 | 6.4958 | NaN | S | NaN | NaN | 2 | NaN | 3101267 | 7 |
| 729 | 730 | 0 | 3 | Ilmakangas, Miss. Pieta Sofia | female | 25.0 | 1 | 0 | STON/O2. 3101271 | 7.9250 | NaN | S | NaN | NaN | 2 | STON | 3101271 | 7 |
| 164 | 165 | 0 | 3 | Panula, Master. Eino Viljami | male | 1.0 | 4 | 1 | 3101295 | 39.6875 | NaN | S | NaN | NaN | 6 | NaN | 3101295 | 7 |
891 rows × 18 columns
sns.kdeplot(data = titantrain, x = 'ticket_length', hue = 'Survived', shade = True)
<Axes: xlabel='ticket_length', ylabel='Density'>
sns.kdeplot(data = titantrain, x = 'ticket_length', hue = 'Pclass', palette = 'bright', shade = True)
<Axes: xlabel='ticket_length', ylabel='Density'>
#Converting the datatype into categorical
titantrain['ticket_length'] = titantrain['ticket_length'].astype('object')
Observations:
# The class distribution of the free tickets
freetix = titantrain.loc[titantrain['Fare'] == 0]
freetix['Pclass'].value_counts()
Pclass 2 6 1 5 3 4 Name: count, dtype: int64
#Seeing if Fare is affected by the class
sns.boxplot(data = titantrain, x = 'Pclass', y = 'Fare')
<Axes: xlabel='Pclass', ylabel='Fare'>
#Seeing if fare or age is affected by age
sns.scatterplot(data= titantrain, x = 'Age', y = 'Fare', hue = 'Sex')
<Axes: xlabel='Age', ylabel='Fare'>
Observations:
As there are presence of very expensive tickets, using median might be more accurate.
#Obtain the median values of each group
medians = titantrain.groupby(['Pclass'])['Fare'].median()
#Let's see what the median values are
print(medians)
Pclass 1 60.2875 2 14.2500 3 8.0500 Name: Fare, dtype: float64
#Assign each median value into each group
median1 = medians.get(1)
median2 = medians.get(2)
median3 = medians.get(3)
#Replace the free tickets' prices with group specific medians
titantrain.loc[(titantrain['Pclass'] == 1) & (titantrain['Fare'] == 0), 'Fare'] = median1
titantrain.loc[(titantrain['Pclass'] == 2) & (titantrain['Fare'] == 0), 'Fare'] = median2
titantrain.loc[(titantrain['Pclass'] == 3) & (titantrain['Fare'] == 0), 'Fare'] = median3
Since there are only 2 null values in 'Embarked' (0.22% of data), we can take a look at these data and see if there are any important trends
#Having a look at the null embarked passengers
no_embarked = titantrain.loc[titantrain['Embarked'].isnull()]
no_embarked
| PassengerId | Survived | Pclass | Name | Sex | Age | SibSp | Parch | Ticket | Fare | Cabin | Embarked | cabin_alphabet | cabin_numbers | family | ticket_alphabet | ticket_numbers | ticket_length | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 61 | 62 | 1 | 1 | Icard, Miss. Amelie | female | 38.0 | 0 | 0 | 113572 | 80.0 | B28 | NaN | B | 28.0 | 1 | NaN | 113572 | 6 |
| 829 | 830 | 1 | 1 | Stone, Mrs. George Nelson (Martha Evelyn) | female | 62.0 | 0 | 0 | 113572 | 80.0 | B28 | NaN | B | 28.0 | 1 | NaN | 113572 | 6 |
#Checking if there are other passengers staying in the same cabin
titantrain.loc[titantrain['Cabin'] == 'B28']
| PassengerId | Survived | Pclass | Name | Sex | Age | SibSp | Parch | Ticket | Fare | Cabin | Embarked | cabin_alphabet | cabin_numbers | family | ticket_alphabet | ticket_numbers | ticket_length | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 61 | 62 | 1 | 1 | Icard, Miss. Amelie | female | 38.0 | 0 | 0 | 113572 | 80.0 | B28 | NaN | B | 28.0 | 1 | NaN | 113572 | 6 |
| 829 | 830 | 1 | 1 | Stone, Mrs. George Nelson (Martha Evelyn) | female | 62.0 | 0 | 0 | 113572 | 80.0 | B28 | NaN | B | 28.0 | 1 | NaN | 113572 | 6 |
Observations:
Although they can be easily substituted with the median value, I just wanted to make sure that they are not any special cases that can contribute to the model building.
#Simplest way is to substitute the null values with the median location
titantrain['Embarked'].value_counts().sort_values(ascending = False)
Embarked S 644 C 168 Q 77 Name: count, dtype: int64
#Making sure that it is not rare for class 1 passengers to survive
titantrain.groupby(['Pclass'])['Survived'].value_counts()
Pclass Survived
1 1 136
0 80
2 0 97
1 87
3 0 372
1 119
Name: count, dtype: int64
#Making sure that their age is not rare for someone who survived.
titantrain.groupby(['Survived'])['Age'].mean()
Survived 0 30.626179 1 28.343690 Name: Age, dtype: float64
titantrain.groupby(['Pclass'])['Embarked'].value_counts()
Pclass Embarked
1 S 127
C 85
Q 2
2 S 164
C 17
Q 3
3 S 353
Q 72
C 66
Name: count, dtype: int64
Observations:
#Substituted the null values with S for SOuthampton
titantrain.loc[titantrain['Embarked'].isna(),'Embarked'] = 'S'
#Check if the null values were substituted
print("There are",titantrain['Embarked'].isnull().sum(),"null values in Embarked")
There are 0 null values in Embarked
As there is a significant number of null values in 'Age', it will be good to identify trends that leads to these values being null. We will then be able to substitute the null values more accurately instead of removing or blindly substituting the data.
Factors to consider:
Here are the conditions we will use to substitute the missing ages:
#See what is the distribution of across the different combination of class and survival status
no_age = titantrain.loc[titantrain['Age'].isnull()]
no_age.groupby(['Pclass'])['Survived'].value_counts()
Pclass Survived
1 0 16
1 14
2 0 7
1 4
3 0 102
1 34
Name: count, dtype: int64
#Defining the values to be substituted according to the conditions
child2 = 14/2 # Class 2 childrens
child3 = 5/2 # Class 3 Childrens' age
median_age = titantrain['Age'].median() # The rest of the passengers with no age
# Substitution of null ages
titantrain.loc[(titantrain['Pclass']) == 3 & (titantrain['Survived'] == 1) & (titantrain['Age'].isnull()), 'Age'] = child3
titantrain.loc[(titantrain['Pclass']) == 2 & (titantrain['Survived'] == 1) & (titantrain['Age'].isnull()), 'Age'] = child2
titantrain.loc[titantrain['Age'].isnull(), 'Age'] = median_age
#Checking if there are still any null values in Age
titantrain['Age'].isnull().sum()
0
As we have uncovered that cabin is not representative of the population, and there were no other clues we can use to estimate the values, we have decided to drop the column all together.
titantrain.head(1)
| PassengerId | Survived | Pclass | Name | Sex | Age | SibSp | Parch | Ticket | Fare | Cabin | Embarked | cabin_alphabet | cabin_numbers | family | ticket_alphabet | ticket_numbers | ticket_length | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 0 | 3 | Braund, Mr. Owen Harris | male | 22.0 | 1 | 0 | A/5 21171 | 7.25 | NaN | S | NaN | NaN | 2 | A | 21171 | 5 |
#Dependent Variable of interest
y = titantrain['Survived']
#Independent variables
features = ['Pclass',
'Sex',
'Age',
'Fare',
'Embarked',
'family',
'ticket_numbers',
'ticket_length']
x = titantrain[features]
x.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 891 entries, 0 to 890 Data columns (total 8 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Pclass 891 non-null object 1 Sex 891 non-null object 2 Age 891 non-null float64 3 Fare 891 non-null float64 4 Embarked 891 non-null object 5 family 891 non-null int64 6 ticket_numbers 891 non-null int64 7 ticket_length 891 non-null object dtypes: float64(2), int64(2), object(4) memory usage: 55.8+ KB
#Performing one-hot encoding for the categorical variables
x = pd.get_dummies(x,
columns = x.select_dtypes(include = ["object","category"]).columns.tolist(),
drop_first = True, dtype = int)
x.head(1)
| Age | Fare | family | ticket_numbers | Pclass_2 | Pclass_3 | Sex_male | Embarked_Q | Embarked_S | ticket_length_3 | ticket_length_4 | ticket_length_5 | ticket_length_6 | ticket_length_7 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 22.0 | 7.25 | 2 | 21171 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 |
# Splitting the dataset into train and test datasets
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.2, shuffle = True, random_state = 1)
print("Shape of Training set : ", x_train.shape)
print("Shape of test set : ", x_test.shape)
Shape of Training set : (712, 14) Shape of test set : (179, 14)
# Scaling the data
sc=StandardScaler()
# Fit_transform on train data
x_train_scaled=sc.fit_transform(x_train)
x_train_scaled=pd.DataFrame(x_train_scaled, columns=x.columns)
# Transform on test data
x_test_scaled=sc.transform(x_test)
x_test_scaled=pd.DataFrame(x_test_scaled, columns=x.columns)
titantest.head()
| PassengerId | Pclass | Name | Sex | Age | SibSp | Parch | Ticket | Fare | Cabin | Embarked | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 892 | 3 | Kelly, Mr. James | male | 34.5 | 0 | 0 | 330911 | 7.8292 | NaN | Q |
| 1 | 893 | 3 | Wilkes, Mrs. James (Ellen Needs) | female | 47.0 | 1 | 0 | 363272 | 7.0000 | NaN | S |
| 2 | 894 | 2 | Myles, Mr. Thomas Francis | male | 62.0 | 0 | 0 | 240276 | 9.6875 | NaN | Q |
| 3 | 895 | 3 | Wirz, Mr. Albert | male | 27.0 | 0 | 0 | 315154 | 8.6625 | NaN | S |
| 4 | 896 | 3 | Hirvonen, Mrs. Alexander (Helga E Lindqvist) | female | 22.0 | 1 | 1 | 3101298 | 12.2875 | NaN | S |
## Changing datatypes
titantest['Pclass'] = titantest['Pclass'].astype('object')
## Free Tickets
#Replace the free and null tickets' prices with group specific medians
titantest.loc[(titantest['Pclass'] == 1) & ((titantest['Fare'] == 0) | (titantest['Fare'].isna())), 'Fare'] = median1
titantest.loc[(titantest['Pclass'] == 2) & ((titantest['Fare'] == 0) | (titantest['Fare'].isna())), 'Fare'] = median2
titantest.loc[(titantest['Pclass'] == 3) & ((titantest['Fare'] == 0) | (titantest['Fare'].isna())), 'Fare'] = median3
print('Free Tickets Substituted with class specific medians')
## Null Age
#Defining the values to be substituted according to the conditions
child2 = 14/2 # Class 2 childrens' age
child3 = 5/2 # Class 3 Childrens' age
median_age = titantest['Age'].median() # The rest of the passengers with no age
# Substitution of null ages
titantest.loc[(titantest['Pclass']) == 3 & (titantest['Age'].isnull()), 'Age'] = child3
titantest.loc[(titantest['Pclass']) == 2 & (titantest['Age'].isnull()), 'Age'] = child2
titantest.loc[titantest['Age'].isnull(), 'Age'] = median_age
print('Null ages substituted with class specific median age')
## Null Embarked values with Southampton
#Substituted the null values with S for Southampton
titantest.loc[titantest['Embarked'].isna(),'Embarked'] = 'S'
#Check if the null values were substituted
print("Null Embarked values substituted with Southampton")
## Ticket Feature Engineering
# Extract alphabets from Ticket and categorize them into single alphabets
titantest['ticket_alphabet'] = titantest['Ticket'].str.extractall(r'([A-Za-z]+)').groupby(level=0)[0].apply(lambda x: ','.join(x))
titantest['ticket_alphabet'] = titantest['ticket_alphabet'].str.split(',').str[0]
#Extract numbers from Ticket and obtaining the ticket number of the passengers with multiple rooms
titantest['ticket_numbers'] = titantest['Ticket'].str.extractall(r'(\d+)').groupby(level=0).agg(lambda x: x.iloc[-1])
titantest['ticket_numbers'] = titantest['ticket_numbers'].apply(lambda x: int(x) if pd.notnull(x) else 0)
#Extract the length of the ticket number
titantest['ticket_length'] = titantest['ticket_numbers'].apply(lambda x:len(str(x)) if pd.notnull(x) else 0)
#Converting the ticket_length datatype into categorical
titantest['ticket_length'] = titantest['ticket_length'].astype('object')
print('Ticket information split into alphabets, numbers and length')
## Family Size
titantest['family'] = titantest['SibSp'] + titantest['Parch'] + 1
print('sibsp and parch combined into family size')
Free Tickets Substituted with class specific medians Null ages substituted with class specific median age Null Embarked values substituted with Southampton Ticket information split into alphabets, numbers and length sibsp and parch combined into family size
test_x = titantest[features]
#Double check test data, see if there are still null values
test_x.isna().sum()
Pclass 0 Sex 0 Age 0 Fare 0 Embarked 0 family 0 ticket_numbers 0 ticket_length 0 dtype: int64
#Performing one-hot encoding for the categorical variables
test_x = pd.get_dummies(test_x,
columns = test_x.select_dtypes(include = ["object","category"]).columns.tolist(),
drop_first = True)
test_x.shape
(418, 14)
# Scaling the data
sc=StandardScaler()
#Scale the data
test_x_scaled=sc.fit_transform(test_x)
test_x_scaled=pd.DataFrame(test_x_scaled, columns=test_x.columns)
#Label the data with the respective ID
test_x_scaled = test_x_scaled.join(titantest['PassengerId'])
#Print out to see what the scaled data look like
test_x_scaled
| Age | Fare | family | ticket_numbers | Pclass_2 | Pclass_3 | Sex_male | Embarked_Q | Embarked_S | ticket_length_3 | ticket_length_4 | ticket_length_5 | ticket_length_6 | ticket_length_7 | PassengerId | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0.408718 | -0.502509 | -0.553443 | 0.132042 | -0.534933 | 0.957826 | 0.755929 | 2.843757 | -1.350676 | -0.120678 | -0.486504 | -0.675608 | 1.133205 | -0.199502 | 892 |
| 1 | 1.349905 | -0.517379 | 0.105643 | 0.187173 | -0.534933 | 0.957826 | -1.322876 | -0.351647 | 0.740370 | -0.120678 | -0.486504 | -0.675608 | 1.133205 | -0.199502 | 893 |
| 2 | 2.479329 | -0.469183 | -0.553443 | -0.022366 | 1.869391 | -1.044031 | 0.755929 | 2.843757 | -1.350676 | -0.120678 | -0.486504 | -0.675608 | 1.133205 | -0.199502 | 894 |
| 3 | -0.155994 | -0.487565 | -0.553443 | 0.105198 | -0.534933 | 0.957826 | 0.755929 | -0.351647 | 0.740370 | -0.120678 | -0.486504 | -0.675608 | 1.133205 | -0.199502 | 895 |
| 4 | -0.532469 | -0.422555 | 0.764728 | 4.851741 | -0.534933 | 0.957826 | -1.322876 | -0.351647 | 0.740370 | -0.120678 | -0.486504 | -0.675608 | -0.882453 | 5.012484 | 896 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 413 | -0.155994 | -0.498549 | -0.553443 | -0.426193 | -0.534933 | 0.957826 | 0.755929 | -0.351647 | 0.740370 | -0.120678 | 2.055480 | -0.675608 | -0.882453 | -0.199502 | 1305 |
| 414 | 0.747545 | 1.310057 | -0.553443 | -0.401453 | -0.534933 | -1.044031 | -1.322876 | -0.351647 | -1.350676 | -0.120678 | -0.486504 | 1.480149 | -0.882453 | -0.199502 | 1306 |
| 415 | 0.709898 | -0.512896 | -0.553443 | 4.851679 | -0.534933 | 0.957826 | 0.755929 | -0.351647 | 0.740370 | -0.120678 | -0.486504 | -0.675608 | -0.882453 | 5.012484 | 1307 |
| 416 | -0.155994 | -0.498549 | -0.553443 | 0.180422 | -0.534933 | 0.957826 | 0.755929 | -0.351647 | 0.740370 | -0.120678 | -0.486504 | -0.675608 | 1.133205 | -0.199502 | 1308 |
| 417 | -0.155994 | -0.241949 | 0.764728 | -0.427161 | -0.534933 | 0.957826 | 0.755929 | -0.351647 | -1.350676 | -0.120678 | 2.055480 | -0.675608 | -0.882453 | -0.199502 | 1309 |
418 rows × 15 columns
# Creating metric function for Classifiers
def metrics_score(actual, predicted):
print(classification_report(actual, predicted))
cm = confusion_matrix(actual, predicted)
plt.figure(figsize=(8,5))
#In this heatmap, make sure the xticklabels are labelled correctly in the format [label if prediction is 0, label if prediction is 1]. In this case, 1 means Satisfied, and 0 means Not Satisfied.
sns.heatmap(cm, annot=True, fmt='.2f', xticklabels=['Died', 'Survived'], yticklabels=['Died', 'Survived'])
plt.ylabel('Actual')
plt.xlabel('Predicted')
plt.show()
# Fixing the seed for random number generators
np.random.seed(42)
import random
random.seed(42)
tf.random.set_seed(42)
# Initialize sequential model
# model_1 = tf.keras.Sequential([tf.keras.layers.Flatten(input_shape=(14,)),#Input layer, 14 parameters
# tf.keras.layers.Dense(64, activation='relu'), #Hidden layer
# tf.keras.layers.Dense(1, activation='sigmoid')]) #Output layer, only 1 node because we only have 1 result to predict
# Initializing the ANN
model_1 = Sequential()
model_1.add(Dense(activation = 'relu', input_dim = 14, units=64))
model_1.add(Dense(1, activation = 'sigmoid'))
#Using the settings for the sequential model above, create the model with the following algorithms
model_1.compile(loss = 'binary_crossentropy',
optimizer='adam',
metrics=['accuracy'])
#Show the model summary
model_1.summary()
Model: "sequential"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
dense (Dense) (None, 64) 960
dense_1 (Dense) (None, 1) 65
=================================================================
Total params: 1,025
Trainable params: 1,025
Non-trainable params: 0
_________________________________________________________________
# Let us now fit the model onto our data
history1 = model_1.fit(x_train_scaled,
y_train,
validation_split=0.2, #20% for validation data
verbose=1, #It writes the verbiage for the training progress. A higher number would give more information
epochs=50, #Number of times the model goes through the entire training dataset
batch_size=32) #This is the batch Stochastic Gradient Descend method, with batchsize per training step
Epoch 1/50 18/18 [==============================] - 3s 43ms/step - loss: 0.6525 - accuracy: 0.6309 - val_loss: 0.6388 - val_accuracy: 0.6573 Epoch 2/50 18/18 [==============================] - 0s 13ms/step - loss: 0.5712 - accuracy: 0.7417 - val_loss: 0.5693 - val_accuracy: 0.6713 Epoch 3/50 18/18 [==============================] - 0s 9ms/step - loss: 0.5204 - accuracy: 0.7979 - val_loss: 0.5232 - val_accuracy: 0.7692 Epoch 4/50 18/18 [==============================] - 0s 13ms/step - loss: 0.4865 - accuracy: 0.8260 - val_loss: 0.4911 - val_accuracy: 0.7832 Epoch 5/50 18/18 [==============================] - 0s 17ms/step - loss: 0.4629 - accuracy: 0.8330 - val_loss: 0.4686 - val_accuracy: 0.7902 Epoch 6/50 18/18 [==============================] - 1s 43ms/step - loss: 0.4447 - accuracy: 0.8366 - val_loss: 0.4535 - val_accuracy: 0.7972 Epoch 7/50 18/18 [==============================] - 1s 35ms/step - loss: 0.4307 - accuracy: 0.8383 - val_loss: 0.4429 - val_accuracy: 0.7972 Epoch 8/50 18/18 [==============================] - 1s 35ms/step - loss: 0.4205 - accuracy: 0.8383 - val_loss: 0.4341 - val_accuracy: 0.8042 Epoch 9/50 18/18 [==============================] - 0s 24ms/step - loss: 0.4126 - accuracy: 0.8401 - val_loss: 0.4305 - val_accuracy: 0.8042 Epoch 10/50 18/18 [==============================] - 1s 40ms/step - loss: 0.4059 - accuracy: 0.8436 - val_loss: 0.4251 - val_accuracy: 0.8252 Epoch 11/50 18/18 [==============================] - 1s 41ms/step - loss: 0.4008 - accuracy: 0.8471 - val_loss: 0.4211 - val_accuracy: 0.8252 Epoch 12/50 18/18 [==============================] - 0s 19ms/step - loss: 0.3959 - accuracy: 0.8489 - val_loss: 0.4195 - val_accuracy: 0.8252 Epoch 13/50 18/18 [==============================] - 0s 17ms/step - loss: 0.3921 - accuracy: 0.8453 - val_loss: 0.4174 - val_accuracy: 0.8322 Epoch 14/50 18/18 [==============================] - 0s 9ms/step - loss: 0.3892 - accuracy: 0.8471 - val_loss: 0.4159 - val_accuracy: 0.8322 Epoch 15/50 18/18 [==============================] - 0s 9ms/step - loss: 0.3870 - accuracy: 0.8489 - val_loss: 0.4149 - val_accuracy: 0.8322 Epoch 16/50 18/18 [==============================] - 0s 8ms/step - loss: 0.3836 - accuracy: 0.8524 - val_loss: 0.4154 - val_accuracy: 0.8322 Epoch 17/50 18/18 [==============================] - 0s 13ms/step - loss: 0.3820 - accuracy: 0.8489 - val_loss: 0.4130 - val_accuracy: 0.8252 Epoch 18/50 18/18 [==============================] - 0s 8ms/step - loss: 0.3796 - accuracy: 0.8524 - val_loss: 0.4128 - val_accuracy: 0.8322 Epoch 19/50 18/18 [==============================] - 0s 14ms/step - loss: 0.3773 - accuracy: 0.8524 - val_loss: 0.4132 - val_accuracy: 0.8322 Epoch 20/50 18/18 [==============================] - 0s 10ms/step - loss: 0.3756 - accuracy: 0.8489 - val_loss: 0.4130 - val_accuracy: 0.8322 Epoch 21/50 18/18 [==============================] - 0s 10ms/step - loss: 0.3741 - accuracy: 0.8524 - val_loss: 0.4135 - val_accuracy: 0.8322 Epoch 22/50 18/18 [==============================] - 0s 11ms/step - loss: 0.3733 - accuracy: 0.8524 - val_loss: 0.4089 - val_accuracy: 0.8322 Epoch 23/50 18/18 [==============================] - 0s 16ms/step - loss: 0.3714 - accuracy: 0.8524 - val_loss: 0.4090 - val_accuracy: 0.8322 Epoch 24/50 18/18 [==============================] - 0s 15ms/step - loss: 0.3706 - accuracy: 0.8524 - val_loss: 0.4101 - val_accuracy: 0.8392 Epoch 25/50 18/18 [==============================] - 0s 11ms/step - loss: 0.3689 - accuracy: 0.8489 - val_loss: 0.4100 - val_accuracy: 0.8322 Epoch 26/50 18/18 [==============================] - 0s 9ms/step - loss: 0.3681 - accuracy: 0.8524 - val_loss: 0.4105 - val_accuracy: 0.8322 Epoch 27/50 18/18 [==============================] - 0s 10ms/step - loss: 0.3662 - accuracy: 0.8541 - val_loss: 0.4102 - val_accuracy: 0.8322 Epoch 28/50 18/18 [==============================] - 0s 16ms/step - loss: 0.3652 - accuracy: 0.8506 - val_loss: 0.4096 - val_accuracy: 0.8322 Epoch 29/50 18/18 [==============================] - 0s 10ms/step - loss: 0.3649 - accuracy: 0.8489 - val_loss: 0.4078 - val_accuracy: 0.8392 Epoch 30/50 18/18 [==============================] - 0s 8ms/step - loss: 0.3638 - accuracy: 0.8489 - val_loss: 0.4076 - val_accuracy: 0.8392 Epoch 31/50 18/18 [==============================] - 0s 8ms/step - loss: 0.3633 - accuracy: 0.8524 - val_loss: 0.4078 - val_accuracy: 0.8392 Epoch 32/50 18/18 [==============================] - 0s 7ms/step - loss: 0.3612 - accuracy: 0.8541 - val_loss: 0.4100 - val_accuracy: 0.8462 Epoch 33/50 18/18 [==============================] - 0s 7ms/step - loss: 0.3608 - accuracy: 0.8489 - val_loss: 0.4089 - val_accuracy: 0.8462 Epoch 34/50 18/18 [==============================] - 0s 6ms/step - loss: 0.3602 - accuracy: 0.8506 - val_loss: 0.4115 - val_accuracy: 0.8392 Epoch 35/50 18/18 [==============================] - 0s 8ms/step - loss: 0.3594 - accuracy: 0.8524 - val_loss: 0.4075 - val_accuracy: 0.8462 Epoch 36/50 18/18 [==============================] - 0s 7ms/step - loss: 0.3585 - accuracy: 0.8506 - val_loss: 0.4091 - val_accuracy: 0.8462 Epoch 37/50 18/18 [==============================] - 0s 6ms/step - loss: 0.3572 - accuracy: 0.8524 - val_loss: 0.4084 - val_accuracy: 0.8462 Epoch 38/50 18/18 [==============================] - 0s 5ms/step - loss: 0.3572 - accuracy: 0.8541 - val_loss: 0.4072 - val_accuracy: 0.8462 Epoch 39/50 18/18 [==============================] - 0s 7ms/step - loss: 0.3560 - accuracy: 0.8541 - val_loss: 0.4087 - val_accuracy: 0.8462 Epoch 40/50 18/18 [==============================] - 0s 8ms/step - loss: 0.3556 - accuracy: 0.8524 - val_loss: 0.4088 - val_accuracy: 0.8462 Epoch 41/50 18/18 [==============================] - 0s 6ms/step - loss: 0.3541 - accuracy: 0.8524 - val_loss: 0.4089 - val_accuracy: 0.8462 Epoch 42/50 18/18 [==============================] - 0s 7ms/step - loss: 0.3543 - accuracy: 0.8524 - val_loss: 0.4086 - val_accuracy: 0.8462 Epoch 43/50 18/18 [==============================] - 0s 8ms/step - loss: 0.3539 - accuracy: 0.8559 - val_loss: 0.4086 - val_accuracy: 0.8462 Epoch 44/50 18/18 [==============================] - 0s 6ms/step - loss: 0.3529 - accuracy: 0.8524 - val_loss: 0.4096 - val_accuracy: 0.8462 Epoch 45/50 18/18 [==============================] - 0s 4ms/step - loss: 0.3531 - accuracy: 0.8559 - val_loss: 0.4093 - val_accuracy: 0.8462 Epoch 46/50 18/18 [==============================] - 0s 4ms/step - loss: 0.3532 - accuracy: 0.8559 - val_loss: 0.4121 - val_accuracy: 0.8392 Epoch 47/50 18/18 [==============================] - 0s 5ms/step - loss: 0.3509 - accuracy: 0.8576 - val_loss: 0.4083 - val_accuracy: 0.8462 Epoch 48/50 18/18 [==============================] - 0s 4ms/step - loss: 0.3509 - accuracy: 0.8559 - val_loss: 0.4093 - val_accuracy: 0.8462 Epoch 49/50 18/18 [==============================] - 0s 4ms/step - loss: 0.3504 - accuracy: 0.8594 - val_loss: 0.4086 - val_accuracy: 0.8462 Epoch 50/50 18/18 [==============================] - 0s 4ms/step - loss: 0.3494 - accuracy: 0.8559 - val_loss: 0.4115 - val_accuracy: 0.8252
#Plotting Train Loss vs Validation Loss
plt.plot(history1.history['loss'])
plt.plot(history1.history['val_loss'])
plt.title('model loss')
plt.ylabel('Loss')
plt.xlabel('Epoch')
plt.legend(['train', 'validation'], loc='upper left')
plt.show()
#Plotting Epoch vs accuracy
plt.plot(history1.history['accuracy'])
plt.plot(history1.history['val_accuracy'])
plt.title('Accuracy vs Epochs')
plt.ylabel('Accuracy')
plt.xlabel('Epoch')
plt.legend(['Train', 'Validation'], loc='lower right')
plt.show()
Observations:
# Using the model to make predictiosn on the training data
y_train_pred = model_1.predict(x_train_scaled)
#Set the threshold of classification to be 0.5
y_train_pred = (y_train_pred > 0.5)
#See what the data looks like
y_train_pred
23/23 [==============================] - 0s 2ms/step
array([[False],
[ True],
[ True],
[False],
[ True],
[ True],
[False],
[False],
[ True],
[False],
[ True],
[False],
[False],
[ True],
[ True],
[ True],
[ True],
[False],
[ True],
[False],
[False],
[False],
[ True],
[False],
[False],
[False],
[ True],
[ True],
[False],
[ True],
[False],
[ True],
[False],
[False],
[False],
[False],
[False],
[False],
[ True],
[ True],
[False],
[ True],
[False],
[False],
[False],
[False],
[False],
[ True],
[False],
[ True],
[False],
[ True],
[ True],
[ True],
[False],
[False],
[False],
[False],
[ True],
[ True],
[False],
[False],
[False],
[False],
[False],
[False],
[False],
[ True],
[ True],
[False],
[ True],
[False],
[False],
[False],
[ True],
[ True],
[ True],
[False],
[ True],
[False],
[False],
[ True],
[False],
[ True],
[ True],
[False],
[False],
[ True],
[False],
[ True],
[ True],
[False],
[False],
[False],
[False],
[False],
[False],
[ True],
[False],
[ True],
[False],
[False],
[False],
[ True],
[False],
[False],
[False],
[ True],
[False],
[False],
[False],
[False],
[False],
[False],
[False],
[False],
[ True],
[ True],
[ True],
[ True],
[False],
[ True],
[False],
[ True],
[False],
[False],
[False],
[ True],
[False],
[ True],
[False],
[False],
[False],
[ True],
[False],
[ True],
[False],
[ True],
[False],
[False],
[False],
[False],
[False],
[False],
[False],
[False],
[ True],
[ True],
[ True],
[False],
[ True],
[ True],
[False],
[False],
[ True],
[False],
[False],
[False],
[ True],
[False],
[ True],
[False],
[False],
[False],
[False],
[False],
[False],
[ True],
[False],
[ True],
[False],
[ True],
[False],
[False],
[False],
[ True],
[ True],
[False],
[False],
[False],
[False],
[False],
[ True],
[False],
[ True],
[False],
[False],
[False],
[False],
[False],
[False],
[ True],
[ True],
[ True],
[ True],
[False],
[False],
[False],
[False],
[False],
[False],
[False],
[False],
[False],
[False],
[False],
[False],
[ True],
[False],
[False],
[False],
[False],
[False],
[ True],
[ True],
[False],
[ True],
[ True],
[ True],
[False],
[False],
[False],
[False],
[False],
[ True],
[False],
[False],
[False],
[False],
[False],
[False],
[False],
[ True],
[False],
[False],
[ True],
[False],
[ True],
[False],
[ True],
[ True],
[ True],
[False],
[False],
[False],
[False],
[ True],
[ True],
[False],
[False],
[False],
[False],
[False],
[ True],
[False],
[ True],
[False],
[ True],
[False],
[False],
[ True],
[False],
[False],
[False],
[ True],
[False],
[False],
[False],
[False],
[False],
[ True],
[ True],
[ True],
[False],
[False],
[False],
[False],
[False],
[ True],
[ True],
[ True],
[False],
[ True],
[False],
[False],
[False],
[False],
[False],
[False],
[False],
[False],
[False],
[False],
[False],
[ True],
[False],
[False],
[False],
[False],
[False],
[ True],
[False],
[False],
[False],
[ True],
[ True],
[False],
[False],
[ True],
[False],
[False],
[ True],
[ True],
[ True],
[ True],
[False],
[False],
[ True],
[False],
[ True],
[False],
[False],
[False],
[ True],
[ True],
[False],
[False],
[False],
[False],
[False],
[False],
[False],
[False],
[ True],
[ True],
[ True],
[False],
[ True],
[False],
[False],
[False],
[ True],
[ True],
[False],
[False],
[ True],
[False],
[False],
[False],
[False],
[False],
[False],
[False],
[False],
[False],
[False],
[False],
[False],
[False],
[False],
[False],
[ True],
[False],
[False],
[False],
[False],
[False],
[False],
[False],
[False],
[False],
[False],
[False],
[False],
[False],
[False],
[False],
[False],
[False],
[ True],
[False],
[ True],
[ True],
[False],
[False],
[ True],
[ True],
[False],
[False],
[False],
[False],
[ True],
[False],
[ True],
[ True],
[False],
[ True],
[False],
[False],
[False],
[ True],
[False],
[False],
[False],
[False],
[False],
[False],
[False],
[ True],
[False],
[ True],
[False],
[False],
[ True],
[False],
[False],
[False],
[ True],
[False],
[False],
[False],
[ True],
[ True],
[False],
[ True],
[False],
[False],
[False],
[ True],
[False],
[ True],
[False],
[False],
[ True],
[False],
[False],
[ True],
[False],
[False],
[ True],
[False],
[False],
[ True],
[False],
[ True],
[False],
[False],
[False],
[False],
[False],
[False],
[False],
[False],
[ True],
[False],
[False],
[False],
[ True],
[False],
[False],
[False],
[False],
[False],
[ True],
[False],
[False],
[ True],
[False],
[False],
[ True],
[False],
[False],
[False],
[False],
[ True],
[False],
[False],
[ True],
[False],
[False],
[ True],
[False],
[ True],
[ True],
[ True],
[False],
[ True],
[ True],
[ True],
[False],
[ True],
[False],
[ True],
[False],
[False],
[False],
[False],
[False],
[ True],
[False],
[False],
[False],
[False],
[False],
[False],
[False],
[False],
[ True],
[False],
[ True],
[False],
[False],
[False],
[False],
[False],
[False],
[ True],
[False],
[ True],
[False],
[False],
[False],
[False],
[False],
[ True],
[ True],
[False],
[False],
[False],
[False],
[False],
[False],
[False],
[False],
[ True],
[False],
[ True],
[False],
[False],
[False],
[False],
[False],
[ True],
[False],
[False],
[False],
[ True],
[False],
[False],
[False],
[False],
[False],
[False],
[False],
[False],
[False],
[False],
[ True],
[ True],
[ True],
[False],
[ True],
[False],
[False],
[ True],
[False],
[False],
[False],
[False],
[False],
[False],
[False],
[False],
[ True],
[False],
[ True],
[False],
[False],
[False],
[False],
[ True],
[ True],
[ True],
[False],
[False],
[ True],
[False],
[False],
[False],
[False],
[False],
[False],
[False],
[False],
[False],
[False],
[False],
[False],
[False],
[False],
[False],
[False],
[ True],
[False],
[ True],
[False],
[False],
[False],
[ True],
[False],
[False],
[ True],
[ True],
[False],
[ True],
[False],
[ True],
[False],
[ True],
[False],
[False],
[False],
[False],
[False],
[False],
[False],
[False],
[ True],
[False],
[False],
[False],
[ True],
[ True],
[False],
[False],
[False],
[ True],
[ True],
[False],
[False],
[False],
[False],
[False],
[ True],
[False],
[False],
[ True],
[ True],
[False],
[ True],
[False],
[False],
[ True],
[False],
[False],
[ True],
[False],
[False],
[False],
[ True],
[False],
[False],
[ True],
[False],
[False],
[False],
[False],
[False],
[ True],
[ True],
[False],
[ True],
[False],
[ True],
[ True],
[False],
[False],
[ True],
[ True],
[False],
[ True],
[False],
[False],
[False],
[False],
[False],
[ True],
[ True],
[False],
[False],
[False],
[False],
[False],
[False],
[ True],
[False],
[ True],
[False],
[False],
[False],
[False],
[False],
[False],
[False],
[False],
[False],
[False],
[False],
[ True],
[False],
[False],
[False]])
metrics_score(y_train,y_train_pred)
precision recall f1-score support
0 0.84 0.94 0.89 443
1 0.88 0.71 0.79 269
accuracy 0.85 712
macro avg 0.86 0.83 0.84 712
weighted avg 0.86 0.85 0.85 712
#Making prediction using the model on the validation data to peformance metric.
y_pred=model_1.predict(x_test_scaled)
#Set the threshold of classification to be 0.5
y_pred = (y_pred > 0.5)
6/6 [==============================] - 0s 3ms/step
metrics_score(y_test,y_pred)
precision recall f1-score support
0 0.78 0.93 0.85 106
1 0.87 0.62 0.72 73
accuracy 0.80 179
macro avg 0.82 0.78 0.78 179
weighted avg 0.81 0.80 0.80 179
Observation:
Before concluding for this first model, we can do one more thing to increase the accuracy. The ROC-AUC tuning method to change the threshold used to classify a prediction.
# predict probabilities
yhat1 = model_1.predict(x_test_scaled)
# keep probabilities for the positive outcome only
yhat1 = yhat1[:, 0]
# calculate roc curves
fpr, tpr, thresholds1 = roc_curve(y_test, yhat1)
# calculate the g-mean for each threshold
gmeans1 = np.sqrt(tpr * (1-fpr))
# locate the index of the largest g-mean
ix = np.argmax(gmeans1)
print('Best Threshold=%f, G-Mean=%.3f' % (thresholds1[ix], gmeans1[ix]))
# plot the roc curve for the model
pyplot.plot([0,1], [0,1], linestyle='--', label='No Skill')
pyplot.plot(fpr, tpr, marker='.')
pyplot.scatter(fpr[ix], tpr[ix], marker='o', color='black', label='Best')
# axis labels
pyplot.xlabel('False Positive Rate')
pyplot.ylabel('True Positive Rate')
pyplot.legend()
# show the plot
pyplot.show()
6/6 [==============================] - 0s 3ms/step Best Threshold=0.307432, G-Mean=0.789
#Making the prediction using the test data
y_pred_e1=model_1.predict(x_test_scaled)
#Using the threshold value to convert the predicted data into true or false statements. If the predicted data is higher than threshold, it will be labelled true.
y_pred_e1 = (y_pred_e1 > thresholds1[ix])
6/6 [==============================] - 0s 3ms/step
metrics_score(y_test, y_pred_e1)
precision recall f1-score support
0 0.83 0.81 0.82 106
1 0.73 0.75 0.74 73
accuracy 0.79 179
macro avg 0.78 0.78 0.78 179
weighted avg 0.79 0.79 0.79 179
# Fixing the seed for random number generators
backend.clear_session()
np.random.seed(42)
random.seed(42)
tf.random.set_seed(42)
# Initialize sequential model
model_2 = tf.keras.Sequential([tf.keras.layers.Flatten(input_shape=(14,)),#Input layer, 14 parameters
tf.keras.layers.Dense(64, activation='leaky_relu'), #Hidden layer
tf.keras.layers.Dense(1, activation='sigmoid')]) #Output layer, only 1 node because we only have 1 result to predict
#Defining the optimizer and learnign rate
optimizer = Adam(learning_rate = 0.001)
#Using the settings for the sequential model above, create the model with the following algorithms
model_2.compile(loss = 'binary_crossentropy',
optimizer = optimizer,
metrics=['accuracy'])
#Show the model summary
model_2.summary()
Model: "sequential"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
flatten (Flatten) (None, 14) 0
dense (Dense) (None, 64) 960
dense_1 (Dense) (None, 1) 65
=================================================================
Total params: 1,025
Trainable params: 1,025
Non-trainable params: 0
_________________________________________________________________
# Let us now fit the model onto our data
history2 = model_2.fit(x_train_scaled,
y_train,
validation_split=0.2, #20% for validation data
verbose=1, #It writes the verbiage for the training progress. A higher number would give more information
epochs=50, #Number of times the model goes through the entire training dataset
batch_size=32) #This is the batch Stochastic Gradient Descend method, with batchsize per training step
Epoch 1/50 18/18 [==============================] - 1s 17ms/step - loss: 0.6428 - accuracy: 0.6450 - val_loss: 0.6298 - val_accuracy: 0.6573 Epoch 2/50 18/18 [==============================] - 0s 4ms/step - loss: 0.5597 - accuracy: 0.7399 - val_loss: 0.5584 - val_accuracy: 0.6573 Epoch 3/50 18/18 [==============================] - 0s 5ms/step - loss: 0.5105 - accuracy: 0.7768 - val_loss: 0.5122 - val_accuracy: 0.7552 Epoch 4/50 18/18 [==============================] - 0s 4ms/step - loss: 0.4784 - accuracy: 0.8207 - val_loss: 0.4823 - val_accuracy: 0.7902 Epoch 5/50 18/18 [==============================] - 0s 5ms/step - loss: 0.4574 - accuracy: 0.8278 - val_loss: 0.4629 - val_accuracy: 0.7972 Epoch 6/50 18/18 [==============================] - 0s 5ms/step - loss: 0.4417 - accuracy: 0.8366 - val_loss: 0.4505 - val_accuracy: 0.8042 Epoch 7/50 18/18 [==============================] - 0s 4ms/step - loss: 0.4298 - accuracy: 0.8366 - val_loss: 0.4424 - val_accuracy: 0.8042 Epoch 8/50 18/18 [==============================] - 0s 5ms/step - loss: 0.4217 - accuracy: 0.8348 - val_loss: 0.4354 - val_accuracy: 0.8042 Epoch 9/50 18/18 [==============================] - 0s 5ms/step - loss: 0.4154 - accuracy: 0.8366 - val_loss: 0.4336 - val_accuracy: 0.8112 Epoch 10/50 18/18 [==============================] - 0s 4ms/step - loss: 0.4102 - accuracy: 0.8348 - val_loss: 0.4297 - val_accuracy: 0.8322 Epoch 11/50 18/18 [==============================] - 0s 4ms/step - loss: 0.4065 - accuracy: 0.8436 - val_loss: 0.4262 - val_accuracy: 0.8182 Epoch 12/50 18/18 [==============================] - 0s 5ms/step - loss: 0.4025 - accuracy: 0.8401 - val_loss: 0.4254 - val_accuracy: 0.8112 Epoch 13/50 18/18 [==============================] - 0s 5ms/step - loss: 0.3994 - accuracy: 0.8453 - val_loss: 0.4235 - val_accuracy: 0.8392 Epoch 14/50 18/18 [==============================] - 0s 4ms/step - loss: 0.3973 - accuracy: 0.8453 - val_loss: 0.4220 - val_accuracy: 0.8252 Epoch 15/50 18/18 [==============================] - 0s 5ms/step - loss: 0.3956 - accuracy: 0.8453 - val_loss: 0.4207 - val_accuracy: 0.8252 Epoch 16/50 18/18 [==============================] - 0s 6ms/step - loss: 0.3925 - accuracy: 0.8453 - val_loss: 0.4218 - val_accuracy: 0.8322 Epoch 17/50 18/18 [==============================] - 0s 4ms/step - loss: 0.3912 - accuracy: 0.8471 - val_loss: 0.4193 - val_accuracy: 0.8252 Epoch 18/50 18/18 [==============================] - 0s 4ms/step - loss: 0.3891 - accuracy: 0.8418 - val_loss: 0.4188 - val_accuracy: 0.8182 Epoch 19/50 18/18 [==============================] - 0s 5ms/step - loss: 0.3872 - accuracy: 0.8453 - val_loss: 0.4190 - val_accuracy: 0.8182 Epoch 20/50 18/18 [==============================] - 0s 4ms/step - loss: 0.3856 - accuracy: 0.8418 - val_loss: 0.4185 - val_accuracy: 0.8182 Epoch 21/50 18/18 [==============================] - 0s 4ms/step - loss: 0.3844 - accuracy: 0.8436 - val_loss: 0.4193 - val_accuracy: 0.8322 Epoch 22/50 18/18 [==============================] - 0s 4ms/step - loss: 0.3838 - accuracy: 0.8436 - val_loss: 0.4139 - val_accuracy: 0.8112 Epoch 23/50 18/18 [==============================] - 0s 4ms/step - loss: 0.3819 - accuracy: 0.8453 - val_loss: 0.4139 - val_accuracy: 0.8112 Epoch 24/50 18/18 [==============================] - 0s 5ms/step - loss: 0.3813 - accuracy: 0.8471 - val_loss: 0.4151 - val_accuracy: 0.8182 Epoch 25/50 18/18 [==============================] - 0s 4ms/step - loss: 0.3796 - accuracy: 0.8436 - val_loss: 0.4141 - val_accuracy: 0.8182 Epoch 26/50 18/18 [==============================] - 0s 5ms/step - loss: 0.3789 - accuracy: 0.8489 - val_loss: 0.4154 - val_accuracy: 0.8252 Epoch 27/50 18/18 [==============================] - 0s 4ms/step - loss: 0.3772 - accuracy: 0.8453 - val_loss: 0.4145 - val_accuracy: 0.8182 Epoch 28/50 18/18 [==============================] - 0s 5ms/step - loss: 0.3762 - accuracy: 0.8453 - val_loss: 0.4133 - val_accuracy: 0.8112 Epoch 29/50 18/18 [==============================] - 0s 5ms/step - loss: 0.3761 - accuracy: 0.8436 - val_loss: 0.4115 - val_accuracy: 0.8252 Epoch 30/50 18/18 [==============================] - 0s 5ms/step - loss: 0.3749 - accuracy: 0.8401 - val_loss: 0.4111 - val_accuracy: 0.8182 Epoch 31/50 18/18 [==============================] - 0s 6ms/step - loss: 0.3746 - accuracy: 0.8453 - val_loss: 0.4106 - val_accuracy: 0.8182 Epoch 32/50 18/18 [==============================] - 0s 5ms/step - loss: 0.3723 - accuracy: 0.8453 - val_loss: 0.4129 - val_accuracy: 0.8392 Epoch 33/50 18/18 [==============================] - 0s 5ms/step - loss: 0.3723 - accuracy: 0.8436 - val_loss: 0.4112 - val_accuracy: 0.8252 Epoch 34/50 18/18 [==============================] - 0s 5ms/step - loss: 0.3718 - accuracy: 0.8401 - val_loss: 0.4140 - val_accuracy: 0.8392 Epoch 35/50 18/18 [==============================] - 0s 5ms/step - loss: 0.3710 - accuracy: 0.8401 - val_loss: 0.4090 - val_accuracy: 0.8252 Epoch 36/50 18/18 [==============================] - 0s 5ms/step - loss: 0.3700 - accuracy: 0.8453 - val_loss: 0.4107 - val_accuracy: 0.8252 Epoch 37/50 18/18 [==============================] - 0s 4ms/step - loss: 0.3687 - accuracy: 0.8436 - val_loss: 0.4097 - val_accuracy: 0.8252 Epoch 38/50 18/18 [==============================] - 0s 4ms/step - loss: 0.3688 - accuracy: 0.8453 - val_loss: 0.4082 - val_accuracy: 0.8252 Epoch 39/50 18/18 [==============================] - 0s 5ms/step - loss: 0.3674 - accuracy: 0.8436 - val_loss: 0.4087 - val_accuracy: 0.8252 Epoch 40/50 18/18 [==============================] - 0s 4ms/step - loss: 0.3672 - accuracy: 0.8418 - val_loss: 0.4092 - val_accuracy: 0.8252 Epoch 41/50 18/18 [==============================] - 0s 5ms/step - loss: 0.3657 - accuracy: 0.8418 - val_loss: 0.4093 - val_accuracy: 0.8252 Epoch 42/50 18/18 [==============================] - 0s 5ms/step - loss: 0.3660 - accuracy: 0.8453 - val_loss: 0.4083 - val_accuracy: 0.8252 Epoch 43/50 18/18 [==============================] - 0s 4ms/step - loss: 0.3657 - accuracy: 0.8471 - val_loss: 0.4079 - val_accuracy: 0.8252 Epoch 44/50 18/18 [==============================] - 0s 5ms/step - loss: 0.3644 - accuracy: 0.8453 - val_loss: 0.4090 - val_accuracy: 0.8252 Epoch 45/50 18/18 [==============================] - 0s 5ms/step - loss: 0.3647 - accuracy: 0.8453 - val_loss: 0.4086 - val_accuracy: 0.8252 Epoch 46/50 18/18 [==============================] - 0s 5ms/step - loss: 0.3648 - accuracy: 0.8453 - val_loss: 0.4105 - val_accuracy: 0.8392 Epoch 47/50 18/18 [==============================] - 0s 5ms/step - loss: 0.3623 - accuracy: 0.8489 - val_loss: 0.4066 - val_accuracy: 0.8252 Epoch 48/50 18/18 [==============================] - 0s 4ms/step - loss: 0.3625 - accuracy: 0.8471 - val_loss: 0.4077 - val_accuracy: 0.8322 Epoch 49/50 18/18 [==============================] - 0s 4ms/step - loss: 0.3622 - accuracy: 0.8524 - val_loss: 0.4070 - val_accuracy: 0.8252 Epoch 50/50 18/18 [==============================] - 0s 4ms/step - loss: 0.3611 - accuracy: 0.8471 - val_loss: 0.4091 - val_accuracy: 0.8252
#Plotting Train Loss vs Validation Loss
plt.plot(history2.history['loss'])
plt.plot(history2.history['val_loss'])
plt.title('model loss')
plt.ylabel('Loss')
plt.xlabel('Epoch')
plt.legend(['train', 'validation'], loc='upper left')
plt.show()
#Plotting Epoch vs accuracy
plt.plot(history2.history['accuracy'])
plt.plot(history2.history['val_accuracy'])
plt.title('Accuracy vs Epochs')
plt.ylabel('Accuracy')
plt.xlabel('Epoch')
plt.legend(['Train', 'Validation'], loc='lower right')
plt.show()
# Using the model to make predictions on the training data
y_train_pred = model_2.predict(x_train_scaled)
#Set the threshold of classification to be 0.5
y_train_pred = (y_train_pred > 0.5)
#Performance of model on training data
metrics_score(y_train,y_train_pred)
23/23 [==============================] - 0s 2ms/step
precision recall f1-score support
0 0.84 0.93 0.88 443
1 0.87 0.70 0.77 269
accuracy 0.85 712
macro avg 0.85 0.82 0.83 712
weighted avg 0.85 0.85 0.84 712
#Making prediction using the model on the validation data to peformance metric.
y_pred=model_2.predict(x_test_scaled)
#Set the threshold of classification to be 0.5
y_pred = (y_pred > 0.5)
#Performance on validation data
metrics_score(y_test,y_pred)
6/6 [==============================] - 0s 3ms/step
precision recall f1-score support
0 0.78 0.93 0.85 106
1 0.87 0.62 0.72 73
accuracy 0.80 179
macro avg 0.82 0.78 0.78 179
weighted avg 0.81 0.80 0.80 179
# predict probabilities
yhat2 = model_2.predict(x_test_scaled)
# keep probabilities for the positive outcome only
yhat2 = yhat2[:, 0]
# calculate roc curves
fpr, tpr, thresholds2 = roc_curve(y_test, yhat2)
# calculate the g-mean for each threshold
gmeans2 = np.sqrt(tpr * (1-fpr))
# locate the index of the largest g-mean
ix = np.argmax(gmeans2)
print('Best Threshold=%f, G-Mean=%.3f' % (thresholds2[ix], gmeans2[ix]))
# plot the roc curve for the model
pyplot.plot([0,1], [0,1], linestyle='--', label='No Skill')
pyplot.plot(fpr, tpr, marker='.')
pyplot.scatter(fpr[ix], tpr[ix], marker='o', color='black', label='Best')
# axis labels
pyplot.xlabel('False Positive Rate')
pyplot.ylabel('True Positive Rate')
pyplot.legend()
# show the plot
pyplot.show()
6/6 [==============================] - 0s 3ms/step Best Threshold=0.392621, G-Mean=0.785
#Making the prediction using the test data
y_pred_e2=model_2.predict(x_test_scaled)
#Using the threshold value to convert the predicted data into true or false statements. If the predicted data is higher than threshold, it will be labelled true.
y_pred_e2 = (y_pred_e2 > thresholds2[ix])
metrics_score(y_test, y_pred_e2)
6/6 [==============================] - 0s 2ms/step
precision recall f1-score support
0 0.81 0.85 0.83 106
1 0.76 0.71 0.74 73
accuracy 0.79 179
macro avg 0.79 0.78 0.78 179
weighted avg 0.79 0.79 0.79 179
Recommendations:
# Fixing the seed for random number generators
backend.clear_session()
np.random.seed(42)
random.seed(42)
tf.random.set_seed(42)
from keras.optimizers import Adamax
# Initialize sequential model
model_3 = tf.keras.Sequential([tf.keras.layers.Flatten(input_shape=(14,)),#Input layer, 14 parameters
tf.keras.layers.Dense(64, activation='leaky_relu'), #Hidden layer
tf.keras.layers.Dropout(0.2), #Dropout 20%
tf.keras.layers.Dense(64, activation='leaky_relu'), #2nd Hidden layer
tf.keras.layers.Dropout(0.2), #Dropout 20%
tf.keras.layers.Dense(1, activation='sigmoid')]) #Output layer, only 1 node because we only have 1 result to predict
#Defining the optimizer and learnign rate
optimizer = Adamax(learning_rate = 0.0005)
#Using the settings for the sequential model above, create the model with the following algorithms
model_3.compile(loss = 'binary_crossentropy',
optimizer = optimizer,
metrics=['accuracy'])
#Show the model summary
model_3.summary()
Model: "sequential"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
flatten (Flatten) (None, 14) 0
dense (Dense) (None, 64) 960
dropout (Dropout) (None, 64) 0
dense_1 (Dense) (None, 64) 4160
dropout_1 (Dropout) (None, 64) 0
dense_2 (Dense) (None, 1) 65
=================================================================
Total params: 5,185
Trainable params: 5,185
Non-trainable params: 0
_________________________________________________________________
# Let us now fit the model onto our data
history3 = model_3.fit(x_train_scaled,
y_train,
validation_split=0.2, #20% for validation data
verbose=1, #It writes the verbiage for the training progress. A higher number would give more information
epochs=100, #Number of times the model goes through the entire training dataset
batch_size=32) #This is the batch Stochastic Gradient Descend method, with batchsize per training step
Epoch 1/100 18/18 [==============================] - 1s 17ms/step - loss: 0.6451 - accuracy: 0.6608 - val_loss: 0.6115 - val_accuracy: 0.7413 Epoch 2/100 18/18 [==============================] - 0s 5ms/step - loss: 0.6097 - accuracy: 0.7135 - val_loss: 0.5841 - val_accuracy: 0.7273 Epoch 3/100 18/18 [==============================] - 0s 5ms/step - loss: 0.5908 - accuracy: 0.7118 - val_loss: 0.5610 - val_accuracy: 0.7203 Epoch 4/100 18/18 [==============================] - 0s 5ms/step - loss: 0.5666 - accuracy: 0.7293 - val_loss: 0.5421 - val_accuracy: 0.7203 Epoch 5/100 18/18 [==============================] - 0s 4ms/step - loss: 0.5526 - accuracy: 0.7399 - val_loss: 0.5254 - val_accuracy: 0.7343 Epoch 6/100 18/18 [==============================] - 0s 5ms/step - loss: 0.5374 - accuracy: 0.7803 - val_loss: 0.5119 - val_accuracy: 0.7622 Epoch 7/100 18/18 [==============================] - 0s 5ms/step - loss: 0.5206 - accuracy: 0.7733 - val_loss: 0.4997 - val_accuracy: 0.7622 Epoch 8/100 18/18 [==============================] - 0s 4ms/step - loss: 0.5173 - accuracy: 0.7645 - val_loss: 0.4893 - val_accuracy: 0.7692 Epoch 9/100 18/18 [==============================] - 0s 5ms/step - loss: 0.5082 - accuracy: 0.7750 - val_loss: 0.4814 - val_accuracy: 0.7692 Epoch 10/100 18/18 [==============================] - 0s 5ms/step - loss: 0.5079 - accuracy: 0.7733 - val_loss: 0.4732 - val_accuracy: 0.7762 Epoch 11/100 18/18 [==============================] - 0s 4ms/step - loss: 0.5030 - accuracy: 0.7698 - val_loss: 0.4663 - val_accuracy: 0.7832 Epoch 12/100 18/18 [==============================] - 0s 4ms/step - loss: 0.4842 - accuracy: 0.7838 - val_loss: 0.4606 - val_accuracy: 0.7762 Epoch 13/100 18/18 [==============================] - 0s 5ms/step - loss: 0.4784 - accuracy: 0.7926 - val_loss: 0.4549 - val_accuracy: 0.7692 Epoch 14/100 18/18 [==============================] - 0s 5ms/step - loss: 0.4782 - accuracy: 0.8049 - val_loss: 0.4502 - val_accuracy: 0.7762 Epoch 15/100 18/18 [==============================] - 0s 4ms/step - loss: 0.4775 - accuracy: 0.7944 - val_loss: 0.4468 - val_accuracy: 0.7902 Epoch 16/100 18/18 [==============================] - 0s 5ms/step - loss: 0.4636 - accuracy: 0.8102 - val_loss: 0.4433 - val_accuracy: 0.7972 Epoch 17/100 18/18 [==============================] - 0s 5ms/step - loss: 0.4690 - accuracy: 0.7909 - val_loss: 0.4399 - val_accuracy: 0.7972 Epoch 18/100 18/18 [==============================] - 0s 4ms/step - loss: 0.4602 - accuracy: 0.8120 - val_loss: 0.4371 - val_accuracy: 0.8042 Epoch 19/100 18/18 [==============================] - 0s 5ms/step - loss: 0.4533 - accuracy: 0.8137 - val_loss: 0.4350 - val_accuracy: 0.8042 Epoch 20/100 18/18 [==============================] - 0s 5ms/step - loss: 0.4533 - accuracy: 0.8102 - val_loss: 0.4333 - val_accuracy: 0.8182 Epoch 21/100 18/18 [==============================] - 0s 4ms/step - loss: 0.4426 - accuracy: 0.8120 - val_loss: 0.4309 - val_accuracy: 0.8182 Epoch 22/100 18/18 [==============================] - 0s 4ms/step - loss: 0.4619 - accuracy: 0.7926 - val_loss: 0.4288 - val_accuracy: 0.8252 Epoch 23/100 18/18 [==============================] - 0s 5ms/step - loss: 0.4399 - accuracy: 0.8049 - val_loss: 0.4271 - val_accuracy: 0.8252 Epoch 24/100 18/18 [==============================] - 0s 4ms/step - loss: 0.4365 - accuracy: 0.8190 - val_loss: 0.4253 - val_accuracy: 0.8252 Epoch 25/100 18/18 [==============================] - 0s 6ms/step - loss: 0.4455 - accuracy: 0.8102 - val_loss: 0.4251 - val_accuracy: 0.8252 Epoch 26/100 18/18 [==============================] - 0s 4ms/step - loss: 0.4416 - accuracy: 0.7961 - val_loss: 0.4233 - val_accuracy: 0.8252 Epoch 27/100 18/18 [==============================] - 0s 5ms/step - loss: 0.4275 - accuracy: 0.8225 - val_loss: 0.4226 - val_accuracy: 0.8252 Epoch 28/100 18/18 [==============================] - 0s 4ms/step - loss: 0.4261 - accuracy: 0.8120 - val_loss: 0.4222 - val_accuracy: 0.8252 Epoch 29/100 18/18 [==============================] - 0s 4ms/step - loss: 0.4226 - accuracy: 0.8225 - val_loss: 0.4220 - val_accuracy: 0.8252 Epoch 30/100 18/18 [==============================] - 0s 5ms/step - loss: 0.4231 - accuracy: 0.8383 - val_loss: 0.4207 - val_accuracy: 0.8252 Epoch 31/100 18/18 [==============================] - 0s 4ms/step - loss: 0.4293 - accuracy: 0.8190 - val_loss: 0.4201 - val_accuracy: 0.8252 Epoch 32/100 18/18 [==============================] - 0s 4ms/step - loss: 0.4231 - accuracy: 0.8120 - val_loss: 0.4206 - val_accuracy: 0.8252 Epoch 33/100 18/18 [==============================] - 0s 4ms/step - loss: 0.4247 - accuracy: 0.8207 - val_loss: 0.4206 - val_accuracy: 0.8252 Epoch 34/100 18/18 [==============================] - 0s 5ms/step - loss: 0.4269 - accuracy: 0.8278 - val_loss: 0.4215 - val_accuracy: 0.8182 Epoch 35/100 18/18 [==============================] - 0s 4ms/step - loss: 0.4139 - accuracy: 0.8278 - val_loss: 0.4209 - val_accuracy: 0.8112 Epoch 36/100 18/18 [==============================] - 0s 6ms/step - loss: 0.4227 - accuracy: 0.8243 - val_loss: 0.4209 - val_accuracy: 0.8112 Epoch 37/100 18/18 [==============================] - 0s 5ms/step - loss: 0.4182 - accuracy: 0.8348 - val_loss: 0.4196 - val_accuracy: 0.8112 Epoch 38/100 18/18 [==============================] - 0s 4ms/step - loss: 0.4211 - accuracy: 0.8190 - val_loss: 0.4186 - val_accuracy: 0.8182 Epoch 39/100 18/18 [==============================] - 0s 4ms/step - loss: 0.4208 - accuracy: 0.8207 - val_loss: 0.4187 - val_accuracy: 0.8112 Epoch 40/100 18/18 [==============================] - 0s 4ms/step - loss: 0.4231 - accuracy: 0.8330 - val_loss: 0.4189 - val_accuracy: 0.8112 Epoch 41/100 18/18 [==============================] - 0s 4ms/step - loss: 0.4246 - accuracy: 0.8155 - val_loss: 0.4186 - val_accuracy: 0.8112 Epoch 42/100 18/18 [==============================] - 0s 4ms/step - loss: 0.4276 - accuracy: 0.8172 - val_loss: 0.4188 - val_accuracy: 0.8112 Epoch 43/100 18/18 [==============================] - 0s 5ms/step - loss: 0.4128 - accuracy: 0.8330 - val_loss: 0.4184 - val_accuracy: 0.8182 Epoch 44/100 18/18 [==============================] - 0s 4ms/step - loss: 0.4334 - accuracy: 0.8190 - val_loss: 0.4180 - val_accuracy: 0.8182 Epoch 45/100 18/18 [==============================] - 0s 4ms/step - loss: 0.4256 - accuracy: 0.8120 - val_loss: 0.4179 - val_accuracy: 0.8252 Epoch 46/100 18/18 [==============================] - 0s 4ms/step - loss: 0.4181 - accuracy: 0.8489 - val_loss: 0.4183 - val_accuracy: 0.8112 Epoch 47/100 18/18 [==============================] - 0s 5ms/step - loss: 0.4169 - accuracy: 0.8225 - val_loss: 0.4170 - val_accuracy: 0.8252 Epoch 48/100 18/18 [==============================] - 0s 5ms/step - loss: 0.4074 - accuracy: 0.8295 - val_loss: 0.4170 - val_accuracy: 0.8252 Epoch 49/100 18/18 [==============================] - 0s 5ms/step - loss: 0.4120 - accuracy: 0.8172 - val_loss: 0.4172 - val_accuracy: 0.8252 Epoch 50/100 18/18 [==============================] - 0s 4ms/step - loss: 0.4083 - accuracy: 0.8348 - val_loss: 0.4184 - val_accuracy: 0.8112 Epoch 51/100 18/18 [==============================] - 0s 4ms/step - loss: 0.4171 - accuracy: 0.8243 - val_loss: 0.4186 - val_accuracy: 0.8112 Epoch 52/100 18/18 [==============================] - 0s 5ms/step - loss: 0.4200 - accuracy: 0.8172 - val_loss: 0.4187 - val_accuracy: 0.8112 Epoch 53/100 18/18 [==============================] - 0s 5ms/step - loss: 0.4118 - accuracy: 0.8225 - val_loss: 0.4194 - val_accuracy: 0.8112 Epoch 54/100 18/18 [==============================] - 0s 5ms/step - loss: 0.4102 - accuracy: 0.8348 - val_loss: 0.4191 - val_accuracy: 0.8112 Epoch 55/100 18/18 [==============================] - 0s 4ms/step - loss: 0.4081 - accuracy: 0.8313 - val_loss: 0.4191 - val_accuracy: 0.8112 Epoch 56/100 18/18 [==============================] - 0s 5ms/step - loss: 0.4040 - accuracy: 0.8471 - val_loss: 0.4194 - val_accuracy: 0.8112 Epoch 57/100 18/18 [==============================] - 0s 5ms/step - loss: 0.4112 - accuracy: 0.8225 - val_loss: 0.4192 - val_accuracy: 0.8112 Epoch 58/100 18/18 [==============================] - 0s 4ms/step - loss: 0.4016 - accuracy: 0.8207 - val_loss: 0.4195 - val_accuracy: 0.7972 Epoch 59/100 18/18 [==============================] - 0s 5ms/step - loss: 0.4098 - accuracy: 0.8102 - val_loss: 0.4192 - val_accuracy: 0.8182 Epoch 60/100 18/18 [==============================] - 0s 5ms/step - loss: 0.4219 - accuracy: 0.8243 - val_loss: 0.4195 - val_accuracy: 0.8042 Epoch 61/100 18/18 [==============================] - 0s 5ms/step - loss: 0.4068 - accuracy: 0.8278 - val_loss: 0.4187 - val_accuracy: 0.8182 Epoch 62/100 18/18 [==============================] - 0s 4ms/step - loss: 0.3960 - accuracy: 0.8383 - val_loss: 0.4188 - val_accuracy: 0.8112 Epoch 63/100 18/18 [==============================] - 0s 4ms/step - loss: 0.3977 - accuracy: 0.8207 - val_loss: 0.4183 - val_accuracy: 0.8112 Epoch 64/100 18/18 [==============================] - 0s 4ms/step - loss: 0.4026 - accuracy: 0.8260 - val_loss: 0.4176 - val_accuracy: 0.8252 Epoch 65/100 18/18 [==============================] - 0s 5ms/step - loss: 0.3964 - accuracy: 0.8295 - val_loss: 0.4186 - val_accuracy: 0.8252 Epoch 66/100 18/18 [==============================] - 0s 4ms/step - loss: 0.4008 - accuracy: 0.8330 - val_loss: 0.4190 - val_accuracy: 0.8112 Epoch 67/100 18/18 [==============================] - 0s 5ms/step - loss: 0.4069 - accuracy: 0.8418 - val_loss: 0.4191 - val_accuracy: 0.7972 Epoch 68/100 18/18 [==============================] - 0s 5ms/step - loss: 0.3992 - accuracy: 0.8225 - val_loss: 0.4191 - val_accuracy: 0.7972 Epoch 69/100 18/18 [==============================] - 0s 5ms/step - loss: 0.4132 - accuracy: 0.8278 - val_loss: 0.4198 - val_accuracy: 0.8112 Epoch 70/100 18/18 [==============================] - 0s 4ms/step - loss: 0.4022 - accuracy: 0.8383 - val_loss: 0.4193 - val_accuracy: 0.8112 Epoch 71/100 18/18 [==============================] - 0s 5ms/step - loss: 0.3974 - accuracy: 0.8295 - val_loss: 0.4190 - val_accuracy: 0.8182 Epoch 72/100 18/18 [==============================] - 0s 5ms/step - loss: 0.4140 - accuracy: 0.8172 - val_loss: 0.4192 - val_accuracy: 0.8182 Epoch 73/100 18/18 [==============================] - 0s 5ms/step - loss: 0.4029 - accuracy: 0.8295 - val_loss: 0.4191 - val_accuracy: 0.8112 Epoch 74/100 18/18 [==============================] - 0s 5ms/step - loss: 0.3990 - accuracy: 0.8278 - val_loss: 0.4196 - val_accuracy: 0.8112 Epoch 75/100 18/18 [==============================] - 0s 6ms/step - loss: 0.3964 - accuracy: 0.8313 - val_loss: 0.4195 - val_accuracy: 0.8182 Epoch 76/100 18/18 [==============================] - 0s 5ms/step - loss: 0.3952 - accuracy: 0.8348 - val_loss: 0.4189 - val_accuracy: 0.8182 Epoch 77/100 18/18 [==============================] - 0s 5ms/step - loss: 0.4127 - accuracy: 0.8278 - val_loss: 0.4186 - val_accuracy: 0.8182 Epoch 78/100 18/18 [==============================] - 0s 4ms/step - loss: 0.4000 - accuracy: 0.8348 - val_loss: 0.4188 - val_accuracy: 0.8252 Epoch 79/100 18/18 [==============================] - 0s 4ms/step - loss: 0.3959 - accuracy: 0.8330 - val_loss: 0.4180 - val_accuracy: 0.8322 Epoch 80/100 18/18 [==============================] - 0s 4ms/step - loss: 0.3881 - accuracy: 0.8366 - val_loss: 0.4181 - val_accuracy: 0.8252 Epoch 81/100 18/18 [==============================] - 0s 4ms/step - loss: 0.3975 - accuracy: 0.8295 - val_loss: 0.4176 - val_accuracy: 0.8322 Epoch 82/100 18/18 [==============================] - 0s 5ms/step - loss: 0.4059 - accuracy: 0.8207 - val_loss: 0.4175 - val_accuracy: 0.8252 Epoch 83/100 18/18 [==============================] - 0s 4ms/step - loss: 0.3976 - accuracy: 0.8383 - val_loss: 0.4181 - val_accuracy: 0.8252 Epoch 84/100 18/18 [==============================] - 0s 6ms/step - loss: 0.3966 - accuracy: 0.8330 - val_loss: 0.4178 - val_accuracy: 0.8182 Epoch 85/100 18/18 [==============================] - 0s 6ms/step - loss: 0.3991 - accuracy: 0.8366 - val_loss: 0.4185 - val_accuracy: 0.8182 Epoch 86/100 18/18 [==============================] - 0s 7ms/step - loss: 0.3952 - accuracy: 0.8295 - val_loss: 0.4187 - val_accuracy: 0.8182 Epoch 87/100 18/18 [==============================] - 0s 5ms/step - loss: 0.4032 - accuracy: 0.8225 - val_loss: 0.4185 - val_accuracy: 0.8182 Epoch 88/100 18/18 [==============================] - 0s 6ms/step - loss: 0.4011 - accuracy: 0.8330 - val_loss: 0.4178 - val_accuracy: 0.8252 Epoch 89/100 18/18 [==============================] - 0s 7ms/step - loss: 0.3900 - accuracy: 0.8260 - val_loss: 0.4177 - val_accuracy: 0.8252 Epoch 90/100 18/18 [==============================] - 0s 7ms/step - loss: 0.3891 - accuracy: 0.8295 - val_loss: 0.4186 - val_accuracy: 0.8252 Epoch 91/100 18/18 [==============================] - 0s 7ms/step - loss: 0.4019 - accuracy: 0.8295 - val_loss: 0.4183 - val_accuracy: 0.8252 Epoch 92/100 18/18 [==============================] - 0s 6ms/step - loss: 0.3850 - accuracy: 0.8348 - val_loss: 0.4186 - val_accuracy: 0.8252 Epoch 93/100 18/18 [==============================] - 0s 7ms/step - loss: 0.3995 - accuracy: 0.8436 - val_loss: 0.4194 - val_accuracy: 0.8252 Epoch 94/100 18/18 [==============================] - 0s 6ms/step - loss: 0.4177 - accuracy: 0.8137 - val_loss: 0.4188 - val_accuracy: 0.8252 Epoch 95/100 18/18 [==============================] - 0s 8ms/step - loss: 0.3873 - accuracy: 0.8524 - val_loss: 0.4188 - val_accuracy: 0.8252 Epoch 96/100 18/18 [==============================] - 0s 7ms/step - loss: 0.3873 - accuracy: 0.8401 - val_loss: 0.4183 - val_accuracy: 0.8252 Epoch 97/100 18/18 [==============================] - 0s 6ms/step - loss: 0.3988 - accuracy: 0.8383 - val_loss: 0.4187 - val_accuracy: 0.8252 Epoch 98/100 18/18 [==============================] - 0s 7ms/step - loss: 0.3790 - accuracy: 0.8436 - val_loss: 0.4191 - val_accuracy: 0.8252 Epoch 99/100 18/18 [==============================] - 0s 8ms/step - loss: 0.3903 - accuracy: 0.8313 - val_loss: 0.4193 - val_accuracy: 0.8252 Epoch 100/100 18/18 [==============================] - 0s 6ms/step - loss: 0.3898 - accuracy: 0.8418 - val_loss: 0.4189 - val_accuracy: 0.8252
#Plotting Train Loss vs Validation Loss
plt.plot(history3.history['loss'])
plt.plot(history3.history['val_loss'])
plt.title('model loss')
plt.ylabel('Loss')
plt.xlabel('Epoch')
plt.legend(['train', 'validation'], loc='upper left')
plt.show()
#Plotting Epoch vs accuracy
plt.plot(history3.history['accuracy'])
plt.plot(history3.history['val_accuracy'])
plt.title('Accuracy vs Epochs')
plt.ylabel('Accuracy')
plt.xlabel('Epoch')
plt.legend(['Train', 'Validation'], loc='lower right')
plt.show()
# Using the model to make predictions on the training data
y_train_pred = model_3.predict(x_train_scaled)
#Set the threshold of classification to be 0.5
y_train_pred = (y_train_pred > 0.5)
#Performance of model on training data
metrics_score(y_train,y_train_pred)
23/23 [==============================] - 0s 2ms/step
precision recall f1-score support
0 0.83 0.93 0.87 443
1 0.85 0.68 0.76 269
accuracy 0.83 712
macro avg 0.84 0.80 0.82 712
weighted avg 0.84 0.83 0.83 712
#Making prediction using the model on the validation data to peformance metric.
y_pred=model_3.predict(x_test_scaled)
#Set the threshold of classification to be 0.5
y_pred = (y_pred > 0.5)
#Performance on validation data
metrics_score(y_test,y_pred)
6/6 [==============================] - 0s 2ms/step
precision recall f1-score support
0 0.78 0.92 0.85 106
1 0.85 0.63 0.72 73
accuracy 0.80 179
macro avg 0.82 0.78 0.79 179
weighted avg 0.81 0.80 0.80 179
# predict probabilities
yhat3 = model_3.predict(x_test_scaled)
# keep probabilities for the positive outcome only
yhat3 = yhat3[:, 0]
# calculate roc curves
fpr, tpr, thresholds3 = roc_curve(y_test, yhat3)
# calculate the g-mean for each threshold
gmeans3 = np.sqrt(tpr * (1-fpr))
# locate the index of the largest g-mean
ix = np.argmax(gmeans3)
print('Best Threshold=%f, G-Mean=%.3f' % (thresholds3[ix], gmeans3[ix]))
# plot the roc curve for the model
pyplot.plot([0,1], [0,1], linestyle='--', label='No Skill')
pyplot.plot(fpr, tpr, marker='.')
pyplot.scatter(fpr[ix], tpr[ix], marker='o', color='black', label='Best')
# axis labels
pyplot.xlabel('False Positive Rate')
pyplot.ylabel('True Positive Rate')
pyplot.legend()
# show the plot
pyplot.show()
6/6 [==============================] - 0s 2ms/step Best Threshold=0.295844, G-Mean=0.798
#Making the prediction using the test data
y_pred_e3=model_3.predict(x_test_scaled)
#Using the threshold value to convert the predicted data into true or false statements. If the predicted data is higher than threshold, it will be labelled true.
y_pred_e3 = (y_pred_e3 > thresholds3[ix])
metrics_score(y_test, y_pred_e3)
6/6 [==============================] - 0s 2ms/step
precision recall f1-score support
0 0.83 0.83 0.83 106
1 0.75 0.75 0.75 73
accuracy 0.80 179
macro avg 0.79 0.79 0.79 179
weighted avg 0.80 0.80 0.80 179
Recommendations:
from keras.layers import Dense, Dropout, Flatten, BatchNormalization
from keras import regularizers
# Fixing the seed for random number generators
backend.clear_session()
np.random.seed(42)
random.seed(42)
tf.random.set_seed(42)
# Initialize sequential model
model_4 = tf.keras.Sequential([tf.keras.layers.Flatten(input_shape=(14,)),#Input layer, 14 parameters
tf.keras.layers.Dense(64, activation='leaky_relu'), #Hidden layer
tf.keras.layers.Dropout(0.2), #Dropout 20%
BatchNormalization(),
tf.keras.layers.Dense(64, activation='leaky_relu'), # 2nd Hidden layer
tf.keras.layers.Dropout(0.2), #Dropout 20%
BatchNormalization(),
tf.keras.layers.Dense(1, activation='sigmoid')]) #Output layer, only 1 node because we only have 1 result to predict
#Defining the optimizer and learnign rate
optimizer = Adamax(learning_rate = 0.0005)
#Using the settings for the sequential model above, create the model with the following algorithms
model_4.compile(loss = 'binary_crossentropy',
optimizer = optimizer,
metrics=['accuracy'])
#Show the model summary
model_4.summary()
Model: "sequential"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
flatten (Flatten) (None, 14) 0
dense (Dense) (None, 64) 960
dropout (Dropout) (None, 64) 0
batch_normalization (BatchN (None, 64) 256
ormalization)
dense_1 (Dense) (None, 64) 4160
dropout_1 (Dropout) (None, 64) 0
batch_normalization_1 (Batc (None, 64) 256
hNormalization)
dense_2 (Dense) (None, 1) 65
=================================================================
Total params: 5,697
Trainable params: 5,441
Non-trainable params: 256
_________________________________________________________________
# Let us now fit the model onto our data
history4 = model_4.fit(x_train_scaled,
y_train,
validation_split=0.2, #20% for validation data
verbose=1, #It writes the verbiage for the training progress. A higher number would give more information
epochs=50, #Number of times the model goes through the entire training dataset
batch_size=64) #This is the batch Stochastic Gradient Descend method, with batchsize per training step
Epoch 1/50 9/9 [==============================] - 1s 41ms/step - loss: 0.7539 - accuracy: 0.6081 - val_loss: 0.6326 - val_accuracy: 0.7203 Epoch 2/50 9/9 [==============================] - 0s 10ms/step - loss: 0.6731 - accuracy: 0.6555 - val_loss: 0.6142 - val_accuracy: 0.7343 Epoch 3/50 9/9 [==============================] - 0s 9ms/step - loss: 0.6617 - accuracy: 0.6643 - val_loss: 0.5974 - val_accuracy: 0.7273 Epoch 4/50 9/9 [==============================] - 0s 8ms/step - loss: 0.6141 - accuracy: 0.6819 - val_loss: 0.5829 - val_accuracy: 0.7273 Epoch 5/50 9/9 [==============================] - 0s 10ms/step - loss: 0.5750 - accuracy: 0.7135 - val_loss: 0.5704 - val_accuracy: 0.7203 Epoch 6/50 9/9 [==============================] - 0s 7ms/step - loss: 0.5549 - accuracy: 0.7329 - val_loss: 0.5587 - val_accuracy: 0.7343 Epoch 7/50 9/9 [==============================] - 0s 7ms/step - loss: 0.5458 - accuracy: 0.7417 - val_loss: 0.5479 - val_accuracy: 0.7483 Epoch 8/50 9/9 [==============================] - 0s 9ms/step - loss: 0.5453 - accuracy: 0.7399 - val_loss: 0.5386 - val_accuracy: 0.7692 Epoch 9/50 9/9 [==============================] - 0s 9ms/step - loss: 0.5345 - accuracy: 0.7399 - val_loss: 0.5299 - val_accuracy: 0.7832 Epoch 10/50 9/9 [==============================] - 0s 9ms/step - loss: 0.5486 - accuracy: 0.7487 - val_loss: 0.5213 - val_accuracy: 0.7832 Epoch 11/50 9/9 [==============================] - 0s 10ms/step - loss: 0.5341 - accuracy: 0.7540 - val_loss: 0.5131 - val_accuracy: 0.7762 Epoch 12/50 9/9 [==============================] - 0s 9ms/step - loss: 0.5028 - accuracy: 0.7627 - val_loss: 0.5065 - val_accuracy: 0.7692 Epoch 13/50 9/9 [==============================] - 0s 7ms/step - loss: 0.5105 - accuracy: 0.7504 - val_loss: 0.4988 - val_accuracy: 0.7762 Epoch 14/50 9/9 [==============================] - 0s 8ms/step - loss: 0.5260 - accuracy: 0.7487 - val_loss: 0.4920 - val_accuracy: 0.7762 Epoch 15/50 9/9 [==============================] - 0s 7ms/step - loss: 0.5096 - accuracy: 0.7768 - val_loss: 0.4863 - val_accuracy: 0.7692 Epoch 16/50 9/9 [==============================] - 0s 10ms/step - loss: 0.4819 - accuracy: 0.7680 - val_loss: 0.4808 - val_accuracy: 0.7692 Epoch 17/50 9/9 [==============================] - 0s 8ms/step - loss: 0.5008 - accuracy: 0.7803 - val_loss: 0.4760 - val_accuracy: 0.7832 Epoch 18/50 9/9 [==============================] - 0s 10ms/step - loss: 0.4951 - accuracy: 0.7715 - val_loss: 0.4704 - val_accuracy: 0.7902 Epoch 19/50 9/9 [==============================] - 0s 9ms/step - loss: 0.5134 - accuracy: 0.7873 - val_loss: 0.4658 - val_accuracy: 0.7832 Epoch 20/50 9/9 [==============================] - 0s 7ms/step - loss: 0.5023 - accuracy: 0.7592 - val_loss: 0.4615 - val_accuracy: 0.7832 Epoch 21/50 9/9 [==============================] - 0s 9ms/step - loss: 0.4774 - accuracy: 0.7768 - val_loss: 0.4584 - val_accuracy: 0.7832 Epoch 22/50 9/9 [==============================] - 0s 10ms/step - loss: 0.4627 - accuracy: 0.7926 - val_loss: 0.4558 - val_accuracy: 0.7832 Epoch 23/50 9/9 [==============================] - 0s 9ms/step - loss: 0.4687 - accuracy: 0.7786 - val_loss: 0.4521 - val_accuracy: 0.7832 Epoch 24/50 9/9 [==============================] - 0s 10ms/step - loss: 0.4696 - accuracy: 0.7838 - val_loss: 0.4494 - val_accuracy: 0.7972 Epoch 25/50 9/9 [==============================] - 0s 8ms/step - loss: 0.4690 - accuracy: 0.8084 - val_loss: 0.4463 - val_accuracy: 0.7972 Epoch 26/50 9/9 [==============================] - 0s 8ms/step - loss: 0.4838 - accuracy: 0.7909 - val_loss: 0.4432 - val_accuracy: 0.8042 Epoch 27/50 9/9 [==============================] - 0s 8ms/step - loss: 0.4544 - accuracy: 0.7909 - val_loss: 0.4409 - val_accuracy: 0.8182 Epoch 28/50 9/9 [==============================] - 0s 10ms/step - loss: 0.4536 - accuracy: 0.8014 - val_loss: 0.4387 - val_accuracy: 0.8112 Epoch 29/50 9/9 [==============================] - 0s 10ms/step - loss: 0.4500 - accuracy: 0.8014 - val_loss: 0.4369 - val_accuracy: 0.8112 Epoch 30/50 9/9 [==============================] - 0s 10ms/step - loss: 0.4694 - accuracy: 0.7909 - val_loss: 0.4348 - val_accuracy: 0.8112 Epoch 31/50 9/9 [==============================] - 0s 7ms/step - loss: 0.4652 - accuracy: 0.8067 - val_loss: 0.4331 - val_accuracy: 0.8112 Epoch 32/50 9/9 [==============================] - 0s 10ms/step - loss: 0.4367 - accuracy: 0.7979 - val_loss: 0.4320 - val_accuracy: 0.8112 Epoch 33/50 9/9 [==============================] - 0s 7ms/step - loss: 0.4607 - accuracy: 0.7909 - val_loss: 0.4312 - val_accuracy: 0.8182 Epoch 34/50 9/9 [==============================] - 0s 7ms/step - loss: 0.4828 - accuracy: 0.7750 - val_loss: 0.4310 - val_accuracy: 0.8182 Epoch 35/50 9/9 [==============================] - 0s 9ms/step - loss: 0.4604 - accuracy: 0.8172 - val_loss: 0.4293 - val_accuracy: 0.8182 Epoch 36/50 9/9 [==============================] - 0s 10ms/step - loss: 0.4476 - accuracy: 0.8014 - val_loss: 0.4278 - val_accuracy: 0.8182 Epoch 37/50 9/9 [==============================] - 0s 9ms/step - loss: 0.4457 - accuracy: 0.8137 - val_loss: 0.4265 - val_accuracy: 0.8182 Epoch 38/50 9/9 [==============================] - 0s 8ms/step - loss: 0.4602 - accuracy: 0.7944 - val_loss: 0.4253 - val_accuracy: 0.8182 Epoch 39/50 9/9 [==============================] - 0s 8ms/step - loss: 0.4886 - accuracy: 0.7926 - val_loss: 0.4238 - val_accuracy: 0.8182 Epoch 40/50 9/9 [==============================] - 0s 8ms/step - loss: 0.4473 - accuracy: 0.8260 - val_loss: 0.4232 - val_accuracy: 0.8182 Epoch 41/50 9/9 [==============================] - 0s 9ms/step - loss: 0.4723 - accuracy: 0.7786 - val_loss: 0.4220 - val_accuracy: 0.8182 Epoch 42/50 9/9 [==============================] - 0s 9ms/step - loss: 0.4608 - accuracy: 0.7961 - val_loss: 0.4220 - val_accuracy: 0.8182 Epoch 43/50 9/9 [==============================] - 0s 9ms/step - loss: 0.4544 - accuracy: 0.7961 - val_loss: 0.4215 - val_accuracy: 0.8182 Epoch 44/50 9/9 [==============================] - 0s 8ms/step - loss: 0.4762 - accuracy: 0.7856 - val_loss: 0.4215 - val_accuracy: 0.8112 Epoch 45/50 9/9 [==============================] - 0s 7ms/step - loss: 0.4750 - accuracy: 0.7944 - val_loss: 0.4217 - val_accuracy: 0.8112 Epoch 46/50 9/9 [==============================] - 0s 8ms/step - loss: 0.4768 - accuracy: 0.7944 - val_loss: 0.4220 - val_accuracy: 0.8112 Epoch 47/50 9/9 [==============================] - 0s 7ms/step - loss: 0.4429 - accuracy: 0.8120 - val_loss: 0.4210 - val_accuracy: 0.8112 Epoch 48/50 9/9 [==============================] - 0s 7ms/step - loss: 0.4288 - accuracy: 0.8225 - val_loss: 0.4204 - val_accuracy: 0.8112 Epoch 49/50 9/9 [==============================] - 0s 10ms/step - loss: 0.4487 - accuracy: 0.7961 - val_loss: 0.4198 - val_accuracy: 0.8112 Epoch 50/50 9/9 [==============================] - 0s 8ms/step - loss: 0.4389 - accuracy: 0.8260 - val_loss: 0.4198 - val_accuracy: 0.8112
#Plotting Train Loss vs Validation Loss
plt.plot(history4.history['loss'])
plt.plot(history4.history['val_loss'])
plt.title('model loss')
plt.ylabel('Loss')
plt.xlabel('Epoch')
plt.legend(['train', 'validation'], loc='upper left')
plt.show()
#Plotting Epoch vs accuracy
plt.plot(history4.history['accuracy'])
plt.plot(history4.history['val_accuracy'])
plt.title('Accuracy vs Epochs')
plt.ylabel('Accuracy')
plt.xlabel('Epoch')
plt.legend(['Train', 'Validation'], loc='lower right')
plt.show()
# Using the model to make predictions on the training data
y_train_pred = model_4.predict(x_train_scaled)
#Set the threshold of classification to be 0.5
y_train_pred = (y_train_pred > 0.5)
#Performance of model on training data
metrics_score(y_train,y_train_pred)
23/23 [==============================] - 0s 2ms/step
precision recall f1-score support
0 0.82 0.93 0.87 443
1 0.86 0.67 0.75 269
accuracy 0.83 712
macro avg 0.84 0.80 0.81 712
weighted avg 0.84 0.83 0.83 712
#Making prediction using the model on the validation data to peformance metric.
y_pred=model_4.predict(x_test_scaled)
#Set the threshold of classification to be 0.5
y_pred = (y_pred > 0.5)
#Performance on validation data
metrics_score(y_test,y_pred)
6/6 [==============================] - 0s 3ms/step
precision recall f1-score support
0 0.78 0.92 0.85 106
1 0.85 0.63 0.72 73
accuracy 0.80 179
macro avg 0.82 0.78 0.79 179
weighted avg 0.81 0.80 0.80 179
# predict probabilities
yhat4 = model_4.predict(x_test_scaled)
# keep probabilities for the positive outcome only
yhat4 = yhat4[:, 0]
# calculate roc curves
fpr, tpr, thresholds4 = roc_curve(y_test, yhat4)
# calculate the g-mean for each threshold
gmeans4 = np.sqrt(tpr * (1-fpr))
# locate the index of the largest g-mean
ix = np.argmax(gmeans4)
print('Best Threshold=%f, G-Mean=%.3f' % (thresholds4[ix], gmeans4[ix]))
# plot the roc curve for the model
pyplot.plot([0,1], [0,1], linestyle='--', label='No Skill')
pyplot.plot(fpr, tpr, marker='.')
pyplot.scatter(fpr[ix], tpr[ix], marker='o', color='black', label='Best')
# axis labels
pyplot.xlabel('False Positive Rate')
pyplot.ylabel('True Positive Rate')
pyplot.legend()
# show the plot
pyplot.show()
6/6 [==============================] - 0s 2ms/step Best Threshold=0.433699, G-Mean=0.795
#Making the prediction using the test data
y_pred_e4=model_4.predict(x_test_scaled)
#Using the threshold value to convert the predicted data into true or false statements. If the predicted data is higher than threshold, it will be labelled true.
y_pred_e4 = (y_pred_e4 > thresholds4[ix])
metrics_score(y_test, y_pred_e4)
6/6 [==============================] - 0s 4ms/step
precision recall f1-score support
0 0.81 0.89 0.85 106
1 0.81 0.70 0.75 73
accuracy 0.81 179
macro avg 0.81 0.79 0.80 179
weighted avg 0.81 0.81 0.81 179
Recommendations:
def build_model(h):
# Clear previous session
backend.clear_session()
np.random.seed(42)
random.seed(42)
tf.random.set_seed(42)
# Initialize sequential model
model = keras.Sequential()
# Add hidden layers (input layer will be adjusted automatically based on shape of input data)
for i in range(h.Int('num_layers', 2, 10)):
model.add(layers.Dense(units=h.Int('units_' + str(i),
min_value=32,
max_value=128,
step=32),
activation='leaky_relu'))
# Add the output layer
model.add(layers.Dense(1, activation='sigmoid'))
# Using the settings for the sequential model above, create the model with the following algorithms
model.compile(optimizer=keras.optimizers.Adamax(
h.Choice('learning_rate', [0.0003, 0.0005, 0.0007])), #Choice() returns a random option from list
loss='binary_crossentropy',
metrics=['accuracy'])
return model
# Install Keras Tuner
!pip install keras-tuner
Collecting keras-tuner
Downloading keras_tuner-1.4.7-py3-none-any.whl (129 kB)
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 129.1/129.1 kB 2.4 MB/s eta 0:00:00
Requirement already satisfied: keras in /usr/local/lib/python3.10/dist-packages (from keras-tuner) (2.12.0)
Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from keras-tuner) (24.0)
Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from keras-tuner) (2.31.0)
Collecting kt-legacy (from keras-tuner)
Downloading kt_legacy-1.0.5-py3-none-any.whl (9.6 kB)
Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->keras-tuner) (3.3.2)
Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->keras-tuner) (3.6)
Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->keras-tuner) (2.0.7)
Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->keras-tuner) (2024.2.2)
Installing collected packages: kt-legacy, keras-tuner
Successfully installed keras-tuner-1.4.7 kt-legacy-1.0.5
from tensorflow import keras
from tensorflow.keras import layers
from kerastuner.tuners import RandomSearch
# Initialie the tuner using randomsearch
tuner = RandomSearch(build_model,
objective='val_accuracy',
max_trials=5,
executions_per_trial=3, #Number of different models to try
project_name='Job_')
# See what combination of values were simulated
tuner.search_space_summary()
Search space summary
Default search space size: 4
num_layers (Int)
{'default': None, 'conditions': [], 'min_value': 2, 'max_value': 10, 'step': 1, 'sampling': 'linear'}
units_0 (Int)
{'default': None, 'conditions': [], 'min_value': 32, 'max_value': 128, 'step': 32, 'sampling': 'linear'}
units_1 (Int)
{'default': None, 'conditions': [], 'min_value': 32, 'max_value': 128, 'step': 32, 'sampling': 'linear'}
learning_rate (Choice)
{'default': 0.0003, 'conditions': [], 'values': [0.0003, 0.0005, 0.0007], 'ordered': True}
# Searching the best model on training data
tuner.search(x_train_scaled, y_train,
epochs=50,
validation_split = 0.2)
Trial 5 Complete [00h 00m 30s] val_accuracy: 0.8251748085021973 Best val_accuracy So Far: 0.8531468510627747 Total elapsed time: 00h 04m 27s
# Printing the best models with their hyperparameters
tuner.results_summary()
Results summary Results in ./Job_ Showing 10 best trials Objective(name="val_accuracy", direction="max") Trial 2 summary Hyperparameters: num_layers: 9 units_0: 96 units_1: 96 learning_rate: 0.0003 units_2: 128 units_3: 96 units_4: 96 units_5: 96 units_6: 64 units_7: 64 units_8: 32 Score: 0.8531468510627747 Trial 3 summary Hyperparameters: num_layers: 9 units_0: 128 units_1: 96 learning_rate: 0.0003 units_2: 64 units_3: 64 units_4: 64 units_5: 64 units_6: 96 units_7: 64 units_8: 32 Score: 0.8531468510627747 Trial 0 summary Hyperparameters: num_layers: 8 units_0: 128 units_1: 64 learning_rate: 0.0005 units_2: 32 units_3: 32 units_4: 32 units_5: 32 units_6: 32 units_7: 32 Score: 0.8461538553237915 Trial 1 summary Hyperparameters: num_layers: 6 units_0: 64 units_1: 64 learning_rate: 0.0003 units_2: 32 units_3: 64 units_4: 96 units_5: 32 units_6: 128 units_7: 32 Score: 0.8321678042411804 Trial 4 summary Hyperparameters: num_layers: 2 units_0: 32 units_1: 32 learning_rate: 0.0005 units_2: 96 units_3: 64 units_4: 96 units_5: 64 units_6: 64 units_7: 32 units_8: 128 Score: 0.8251748085021973
## Create model based on tuner results
# Clear session
backend.clear_session()
np.random.seed(42)
random.seed(42)
tf.random.set_seed(42)
# Initialize sequential model
modelkeras = tf.keras.Sequential([tf.keras.layers.Flatten(input_shape=(14,)),#Input layer, 14 parameters
tf.keras.layers.Dense(128, activation='leaky_relu'), # 0 Hidden layer
tf.keras.layers.Dense(96, activation='leaky_relu'), # 1 Hidden layer
tf.keras.layers.Dense(64, activation='leaky_relu'), # 2 Hidden layer
tf.keras.layers.Dense(64, activation='leaky_relu'), # 3 Hidden layer
tf.keras.layers.Dense(64, activation='leaky_relu'), # 4 Hidden layer
tf.keras.layers.Dense(64, activation='leaky_relu'), # 5 Hidden layer
tf.keras.layers.Dense(96, activation='leaky_relu'), # 6 Hidden layer
tf.keras.layers.Dense(64, activation='leaky_relu'), # 7 Hidden layer
tf.keras.layers.Dense(32, activation='leaky_relu'), # 8 Hidden layer
tf.keras.layers.Dense(1, activation='sigmoid')]) #Output layer, only 1 node because we only have 1 result to predict
# Compile model using oparameters
optimizer = tf.keras.optimizers.Adamax(0.0003)
modelkeras.compile(loss='binary_crossentropy',optimizer=optimizer,metrics=['accuracy'])
# See summary
modelkeras.summary()
Model: "sequential"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
flatten (Flatten) (None, 14) 0
dense (Dense) (None, 128) 1920
dense_1 (Dense) (None, 96) 12384
dense_2 (Dense) (None, 64) 6208
dense_3 (Dense) (None, 64) 4160
dense_4 (Dense) (None, 64) 4160
dense_5 (Dense) (None, 64) 4160
dense_6 (Dense) (None, 96) 6240
dense_7 (Dense) (None, 64) 6208
dense_8 (Dense) (None, 32) 2080
dense_9 (Dense) (None, 1) 33
=================================================================
Total params: 47,553
Trainable params: 47,553
Non-trainable params: 0
_________________________________________________________________
history_keras = modelkeras.fit(x_train_scaled,
y_train,
batch_size=64,
epochs=150,
verbose=1,
validation_split = 0.2)
Epoch 1/150 9/9 [==============================] - 5s 118ms/step - loss: 0.6835 - accuracy: 0.6134 - val_loss: 0.6685 - val_accuracy: 0.6573 Epoch 2/150 9/9 [==============================] - 0s 25ms/step - loss: 0.6659 - accuracy: 0.6134 - val_loss: 0.6463 - val_accuracy: 0.6573 Epoch 3/150 9/9 [==============================] - 0s 27ms/step - loss: 0.6434 - accuracy: 0.6134 - val_loss: 0.6185 - val_accuracy: 0.6573 Epoch 4/150 9/9 [==============================] - 0s 37ms/step - loss: 0.6185 - accuracy: 0.6134 - val_loss: 0.5902 - val_accuracy: 0.6643 Epoch 5/150 9/9 [==============================] - 0s 25ms/step - loss: 0.5926 - accuracy: 0.6661 - val_loss: 0.5604 - val_accuracy: 0.7273 Epoch 6/150 9/9 [==============================] - 0s 28ms/step - loss: 0.5671 - accuracy: 0.7311 - val_loss: 0.5366 - val_accuracy: 0.7552 Epoch 7/150 9/9 [==============================] - 0s 18ms/step - loss: 0.5490 - accuracy: 0.7575 - val_loss: 0.5173 - val_accuracy: 0.7902 Epoch 8/150 9/9 [==============================] - 0s 28ms/step - loss: 0.5317 - accuracy: 0.7891 - val_loss: 0.5027 - val_accuracy: 0.7972 Epoch 9/150 9/9 [==============================] - 0s 28ms/step - loss: 0.5171 - accuracy: 0.8190 - val_loss: 0.4911 - val_accuracy: 0.7762 Epoch 10/150 9/9 [==============================] - 0s 20ms/step - loss: 0.5029 - accuracy: 0.8243 - val_loss: 0.4793 - val_accuracy: 0.7762 Epoch 11/150 9/9 [==============================] - 0s 17ms/step - loss: 0.4899 - accuracy: 0.8243 - val_loss: 0.4684 - val_accuracy: 0.7902 Epoch 12/150 9/9 [==============================] - 0s 17ms/step - loss: 0.4765 - accuracy: 0.8295 - val_loss: 0.4600 - val_accuracy: 0.7902 Epoch 13/150 9/9 [==============================] - 0s 15ms/step - loss: 0.4627 - accuracy: 0.8383 - val_loss: 0.4517 - val_accuracy: 0.8042 Epoch 14/150 9/9 [==============================] - 0s 17ms/step - loss: 0.4496 - accuracy: 0.8418 - val_loss: 0.4434 - val_accuracy: 0.8042 Epoch 15/150 9/9 [==============================] - 0s 16ms/step - loss: 0.4368 - accuracy: 0.8436 - val_loss: 0.4377 - val_accuracy: 0.8042 Epoch 16/150 9/9 [==============================] - 0s 17ms/step - loss: 0.4238 - accuracy: 0.8453 - val_loss: 0.4325 - val_accuracy: 0.8042 Epoch 17/150 9/9 [==============================] - 0s 17ms/step - loss: 0.4128 - accuracy: 0.8471 - val_loss: 0.4283 - val_accuracy: 0.8042 Epoch 18/150 9/9 [==============================] - 0s 16ms/step - loss: 0.4029 - accuracy: 0.8471 - val_loss: 0.4264 - val_accuracy: 0.8112 Epoch 19/150 9/9 [==============================] - 0s 17ms/step - loss: 0.3938 - accuracy: 0.8489 - val_loss: 0.4252 - val_accuracy: 0.8182 Epoch 20/150 9/9 [==============================] - 0s 14ms/step - loss: 0.3868 - accuracy: 0.8489 - val_loss: 0.4246 - val_accuracy: 0.8252 Epoch 21/150 9/9 [==============================] - 0s 15ms/step - loss: 0.3808 - accuracy: 0.8471 - val_loss: 0.4245 - val_accuracy: 0.8252 Epoch 22/150 9/9 [==============================] - 0s 16ms/step - loss: 0.3761 - accuracy: 0.8506 - val_loss: 0.4229 - val_accuracy: 0.8252 Epoch 23/150 9/9 [==============================] - 0s 14ms/step - loss: 0.3720 - accuracy: 0.8524 - val_loss: 0.4224 - val_accuracy: 0.8252 Epoch 24/150 9/9 [==============================] - 0s 16ms/step - loss: 0.3695 - accuracy: 0.8559 - val_loss: 0.4209 - val_accuracy: 0.8252 Epoch 25/150 9/9 [==============================] - 0s 16ms/step - loss: 0.3658 - accuracy: 0.8541 - val_loss: 0.4203 - val_accuracy: 0.8252 Epoch 26/150 9/9 [==============================] - 0s 17ms/step - loss: 0.3635 - accuracy: 0.8541 - val_loss: 0.4193 - val_accuracy: 0.8252 Epoch 27/150 9/9 [==============================] - 0s 15ms/step - loss: 0.3599 - accuracy: 0.8559 - val_loss: 0.4201 - val_accuracy: 0.8252 Epoch 28/150 9/9 [==============================] - 0s 11ms/step - loss: 0.3586 - accuracy: 0.8559 - val_loss: 0.4193 - val_accuracy: 0.8182 Epoch 29/150 9/9 [==============================] - 0s 10ms/step - loss: 0.3567 - accuracy: 0.8559 - val_loss: 0.4158 - val_accuracy: 0.8182 Epoch 30/150 9/9 [==============================] - 0s 10ms/step - loss: 0.3545 - accuracy: 0.8594 - val_loss: 0.4151 - val_accuracy: 0.8182 Epoch 31/150 9/9 [==============================] - 0s 9ms/step - loss: 0.3527 - accuracy: 0.8576 - val_loss: 0.4140 - val_accuracy: 0.8182 Epoch 32/150 9/9 [==============================] - 0s 10ms/step - loss: 0.3509 - accuracy: 0.8594 - val_loss: 0.4137 - val_accuracy: 0.8182 Epoch 33/150 9/9 [==============================] - 0s 12ms/step - loss: 0.3487 - accuracy: 0.8612 - val_loss: 0.4123 - val_accuracy: 0.8252 Epoch 34/150 9/9 [==============================] - 0s 10ms/step - loss: 0.3473 - accuracy: 0.8594 - val_loss: 0.4128 - val_accuracy: 0.8322 Epoch 35/150 9/9 [==============================] - 0s 11ms/step - loss: 0.3460 - accuracy: 0.8612 - val_loss: 0.4110 - val_accuracy: 0.8182 Epoch 36/150 9/9 [==============================] - 0s 9ms/step - loss: 0.3450 - accuracy: 0.8559 - val_loss: 0.4109 - val_accuracy: 0.8182 Epoch 37/150 9/9 [==============================] - 0s 8ms/step - loss: 0.3426 - accuracy: 0.8629 - val_loss: 0.4088 - val_accuracy: 0.8322 Epoch 38/150 9/9 [==============================] - 0s 11ms/step - loss: 0.3424 - accuracy: 0.8647 - val_loss: 0.4077 - val_accuracy: 0.8322 Epoch 39/150 9/9 [==============================] - 0s 9ms/step - loss: 0.3412 - accuracy: 0.8559 - val_loss: 0.4079 - val_accuracy: 0.8112 Epoch 40/150 9/9 [==============================] - 0s 10ms/step - loss: 0.3396 - accuracy: 0.8612 - val_loss: 0.4062 - val_accuracy: 0.8322 Epoch 41/150 9/9 [==============================] - 0s 9ms/step - loss: 0.3377 - accuracy: 0.8664 - val_loss: 0.4061 - val_accuracy: 0.8322 Epoch 42/150 9/9 [==============================] - 0s 8ms/step - loss: 0.3366 - accuracy: 0.8594 - val_loss: 0.4058 - val_accuracy: 0.8112 Epoch 43/150 9/9 [==============================] - 0s 8ms/step - loss: 0.3355 - accuracy: 0.8629 - val_loss: 0.4045 - val_accuracy: 0.8322 Epoch 44/150 9/9 [==============================] - 0s 8ms/step - loss: 0.3353 - accuracy: 0.8647 - val_loss: 0.4047 - val_accuracy: 0.8392 Epoch 45/150 9/9 [==============================] - 0s 10ms/step - loss: 0.3344 - accuracy: 0.8664 - val_loss: 0.4051 - val_accuracy: 0.8322 Epoch 46/150 9/9 [==============================] - 0s 9ms/step - loss: 0.3338 - accuracy: 0.8629 - val_loss: 0.4049 - val_accuracy: 0.8462 Epoch 47/150 9/9 [==============================] - 0s 11ms/step - loss: 0.3309 - accuracy: 0.8717 - val_loss: 0.4026 - val_accuracy: 0.8392 Epoch 48/150 9/9 [==============================] - 0s 10ms/step - loss: 0.3310 - accuracy: 0.8682 - val_loss: 0.4014 - val_accuracy: 0.8322 Epoch 49/150 9/9 [==============================] - 0s 9ms/step - loss: 0.3280 - accuracy: 0.8717 - val_loss: 0.4019 - val_accuracy: 0.8112 Epoch 50/150 9/9 [==============================] - 0s 8ms/step - loss: 0.3283 - accuracy: 0.8770 - val_loss: 0.4019 - val_accuracy: 0.8322 Epoch 51/150 9/9 [==============================] - 0s 9ms/step - loss: 0.3289 - accuracy: 0.8717 - val_loss: 0.4021 - val_accuracy: 0.8462 Epoch 52/150 9/9 [==============================] - 0s 10ms/step - loss: 0.3261 - accuracy: 0.8735 - val_loss: 0.4023 - val_accuracy: 0.8322 Epoch 53/150 9/9 [==============================] - 0s 11ms/step - loss: 0.3239 - accuracy: 0.8787 - val_loss: 0.4026 - val_accuracy: 0.8322 Epoch 54/150 9/9 [==============================] - 0s 9ms/step - loss: 0.3251 - accuracy: 0.8735 - val_loss: 0.4000 - val_accuracy: 0.8322 Epoch 55/150 9/9 [==============================] - 0s 8ms/step - loss: 0.3235 - accuracy: 0.8717 - val_loss: 0.4020 - val_accuracy: 0.8322 Epoch 56/150 9/9 [==============================] - 0s 9ms/step - loss: 0.3206 - accuracy: 0.8805 - val_loss: 0.4008 - val_accuracy: 0.8322 Epoch 57/150 9/9 [==============================] - 0s 8ms/step - loss: 0.3192 - accuracy: 0.8752 - val_loss: 0.4007 - val_accuracy: 0.8322 Epoch 58/150 9/9 [==============================] - 0s 9ms/step - loss: 0.3191 - accuracy: 0.8735 - val_loss: 0.4022 - val_accuracy: 0.8392 Epoch 59/150 9/9 [==============================] - 0s 10ms/step - loss: 0.3185 - accuracy: 0.8787 - val_loss: 0.4003 - val_accuracy: 0.8462 Epoch 60/150 9/9 [==============================] - 0s 9ms/step - loss: 0.3182 - accuracy: 0.8840 - val_loss: 0.4029 - val_accuracy: 0.8531 Epoch 61/150 9/9 [==============================] - 0s 9ms/step - loss: 0.3151 - accuracy: 0.8822 - val_loss: 0.4018 - val_accuracy: 0.8462 Epoch 62/150 9/9 [==============================] - 0s 9ms/step - loss: 0.3135 - accuracy: 0.8805 - val_loss: 0.4026 - val_accuracy: 0.8462 Epoch 63/150 9/9 [==============================] - 0s 8ms/step - loss: 0.3135 - accuracy: 0.8805 - val_loss: 0.4010 - val_accuracy: 0.8392 Epoch 64/150 9/9 [==============================] - 0s 9ms/step - loss: 0.3134 - accuracy: 0.8858 - val_loss: 0.4019 - val_accuracy: 0.8392 Epoch 65/150 9/9 [==============================] - 0s 10ms/step - loss: 0.3125 - accuracy: 0.8822 - val_loss: 0.4041 - val_accuracy: 0.8531 Epoch 66/150 9/9 [==============================] - 0s 10ms/step - loss: 0.3106 - accuracy: 0.8840 - val_loss: 0.4035 - val_accuracy: 0.8531 Epoch 67/150 9/9 [==============================] - 0s 8ms/step - loss: 0.3079 - accuracy: 0.8946 - val_loss: 0.4032 - val_accuracy: 0.8531 Epoch 68/150 9/9 [==============================] - 0s 8ms/step - loss: 0.3071 - accuracy: 0.8928 - val_loss: 0.4017 - val_accuracy: 0.8531 Epoch 69/150 9/9 [==============================] - 0s 10ms/step - loss: 0.3071 - accuracy: 0.8893 - val_loss: 0.4026 - val_accuracy: 0.8462 Epoch 70/150 9/9 [==============================] - 0s 10ms/step - loss: 0.3061 - accuracy: 0.8893 - val_loss: 0.4060 - val_accuracy: 0.8531 Epoch 71/150 9/9 [==============================] - 0s 10ms/step - loss: 0.3048 - accuracy: 0.8928 - val_loss: 0.4049 - val_accuracy: 0.8671 Epoch 72/150 9/9 [==============================] - 0s 9ms/step - loss: 0.3043 - accuracy: 0.8946 - val_loss: 0.4056 - val_accuracy: 0.8462 Epoch 73/150 9/9 [==============================] - 0s 11ms/step - loss: 0.3025 - accuracy: 0.8946 - val_loss: 0.4064 - val_accuracy: 0.8671 Epoch 74/150 9/9 [==============================] - 0s 10ms/step - loss: 0.3017 - accuracy: 0.8998 - val_loss: 0.4070 - val_accuracy: 0.8601 Epoch 75/150 9/9 [==============================] - 0s 9ms/step - loss: 0.3011 - accuracy: 0.8946 - val_loss: 0.4092 - val_accuracy: 0.8462 Epoch 76/150 9/9 [==============================] - 0s 8ms/step - loss: 0.3000 - accuracy: 0.8928 - val_loss: 0.4104 - val_accuracy: 0.8601 Epoch 77/150 9/9 [==============================] - 0s 10ms/step - loss: 0.2987 - accuracy: 0.8998 - val_loss: 0.4076 - val_accuracy: 0.8531 Epoch 78/150 9/9 [==============================] - 0s 10ms/step - loss: 0.2985 - accuracy: 0.9016 - val_loss: 0.4104 - val_accuracy: 0.8531 Epoch 79/150 9/9 [==============================] - 0s 11ms/step - loss: 0.2964 - accuracy: 0.8963 - val_loss: 0.4119 - val_accuracy: 0.8531 Epoch 80/150 9/9 [==============================] - 0s 10ms/step - loss: 0.2963 - accuracy: 0.8963 - val_loss: 0.4133 - val_accuracy: 0.8601 Epoch 81/150 9/9 [==============================] - 0s 10ms/step - loss: 0.2955 - accuracy: 0.9033 - val_loss: 0.4117 - val_accuracy: 0.8531 Epoch 82/150 9/9 [==============================] - 0s 9ms/step - loss: 0.2947 - accuracy: 0.9033 - val_loss: 0.4138 - val_accuracy: 0.8601 Epoch 83/150 9/9 [==============================] - 0s 9ms/step - loss: 0.2943 - accuracy: 0.9016 - val_loss: 0.4175 - val_accuracy: 0.8462 Epoch 84/150 9/9 [==============================] - 0s 9ms/step - loss: 0.2938 - accuracy: 0.8998 - val_loss: 0.4155 - val_accuracy: 0.8462 Epoch 85/150 9/9 [==============================] - 0s 10ms/step - loss: 0.2927 - accuracy: 0.8963 - val_loss: 0.4198 - val_accuracy: 0.8531 Epoch 86/150 9/9 [==============================] - 0s 11ms/step - loss: 0.2912 - accuracy: 0.9033 - val_loss: 0.4184 - val_accuracy: 0.8462 Epoch 87/150 9/9 [==============================] - 0s 11ms/step - loss: 0.2907 - accuracy: 0.8998 - val_loss: 0.4209 - val_accuracy: 0.8531 Epoch 88/150 9/9 [==============================] - 0s 11ms/step - loss: 0.2901 - accuracy: 0.9051 - val_loss: 0.4197 - val_accuracy: 0.8462 Epoch 89/150 9/9 [==============================] - 0s 11ms/step - loss: 0.2892 - accuracy: 0.9033 - val_loss: 0.4200 - val_accuracy: 0.8531 Epoch 90/150 9/9 [==============================] - 0s 11ms/step - loss: 0.2889 - accuracy: 0.9033 - val_loss: 0.4247 - val_accuracy: 0.8531 Epoch 91/150 9/9 [==============================] - 0s 9ms/step - loss: 0.2871 - accuracy: 0.9051 - val_loss: 0.4220 - val_accuracy: 0.8531 Epoch 92/150 9/9 [==============================] - 0s 9ms/step - loss: 0.2862 - accuracy: 0.9051 - val_loss: 0.4216 - val_accuracy: 0.8601 Epoch 93/150 9/9 [==============================] - 0s 10ms/step - loss: 0.2859 - accuracy: 0.9051 - val_loss: 0.4261 - val_accuracy: 0.8531 Epoch 94/150 9/9 [==============================] - 0s 11ms/step - loss: 0.2871 - accuracy: 0.9016 - val_loss: 0.4281 - val_accuracy: 0.8462 Epoch 95/150 9/9 [==============================] - 0s 11ms/step - loss: 0.2867 - accuracy: 0.8998 - val_loss: 0.4295 - val_accuracy: 0.8531 Epoch 96/150 9/9 [==============================] - 0s 9ms/step - loss: 0.2909 - accuracy: 0.9051 - val_loss: 0.4253 - val_accuracy: 0.8531 Epoch 97/150 9/9 [==============================] - 0s 8ms/step - loss: 0.2832 - accuracy: 0.9069 - val_loss: 0.4316 - val_accuracy: 0.8462 Epoch 98/150 9/9 [==============================] - 0s 9ms/step - loss: 0.2818 - accuracy: 0.9033 - val_loss: 0.4284 - val_accuracy: 0.8531 Epoch 99/150 9/9 [==============================] - 0s 9ms/step - loss: 0.2813 - accuracy: 0.9086 - val_loss: 0.4298 - val_accuracy: 0.8462 Epoch 100/150 9/9 [==============================] - 0s 11ms/step - loss: 0.2805 - accuracy: 0.9104 - val_loss: 0.4337 - val_accuracy: 0.8392 Epoch 101/150 9/9 [==============================] - 0s 8ms/step - loss: 0.2797 - accuracy: 0.9069 - val_loss: 0.4328 - val_accuracy: 0.8531 Epoch 102/150 9/9 [==============================] - 0s 11ms/step - loss: 0.2790 - accuracy: 0.9069 - val_loss: 0.4369 - val_accuracy: 0.8462 Epoch 103/150 9/9 [==============================] - 0s 9ms/step - loss: 0.2789 - accuracy: 0.9104 - val_loss: 0.4349 - val_accuracy: 0.8531 Epoch 104/150 9/9 [==============================] - 0s 13ms/step - loss: 0.2775 - accuracy: 0.9086 - val_loss: 0.4376 - val_accuracy: 0.8462 Epoch 105/150 9/9 [==============================] - 0s 9ms/step - loss: 0.2813 - accuracy: 0.8981 - val_loss: 0.4407 - val_accuracy: 0.8462 Epoch 106/150 9/9 [==============================] - 0s 10ms/step - loss: 0.2770 - accuracy: 0.9051 - val_loss: 0.4350 - val_accuracy: 0.8462 Epoch 107/150 9/9 [==============================] - 0s 11ms/step - loss: 0.2772 - accuracy: 0.9086 - val_loss: 0.4451 - val_accuracy: 0.8462 Epoch 108/150 9/9 [==============================] - 0s 16ms/step - loss: 0.2763 - accuracy: 0.9051 - val_loss: 0.4422 - val_accuracy: 0.8392 Epoch 109/150 9/9 [==============================] - 0s 15ms/step - loss: 0.2748 - accuracy: 0.9121 - val_loss: 0.4450 - val_accuracy: 0.8462 Epoch 110/150 9/9 [==============================] - 0s 16ms/step - loss: 0.2758 - accuracy: 0.9086 - val_loss: 0.4408 - val_accuracy: 0.8462 Epoch 111/150 9/9 [==============================] - 0s 12ms/step - loss: 0.2756 - accuracy: 0.9086 - val_loss: 0.4493 - val_accuracy: 0.8392 Epoch 112/150 9/9 [==============================] - 0s 14ms/step - loss: 0.2752 - accuracy: 0.9086 - val_loss: 0.4455 - val_accuracy: 0.8462 Epoch 113/150 9/9 [==============================] - 0s 14ms/step - loss: 0.2740 - accuracy: 0.9121 - val_loss: 0.4477 - val_accuracy: 0.8392 Epoch 114/150 9/9 [==============================] - 0s 13ms/step - loss: 0.2720 - accuracy: 0.9069 - val_loss: 0.4497 - val_accuracy: 0.8392 Epoch 115/150 9/9 [==============================] - 0s 13ms/step - loss: 0.2699 - accuracy: 0.9104 - val_loss: 0.4491 - val_accuracy: 0.8392 Epoch 116/150 9/9 [==============================] - 0s 14ms/step - loss: 0.2711 - accuracy: 0.9086 - val_loss: 0.4524 - val_accuracy: 0.8462 Epoch 117/150 9/9 [==============================] - 0s 17ms/step - loss: 0.2721 - accuracy: 0.9016 - val_loss: 0.4528 - val_accuracy: 0.8462 Epoch 118/150 9/9 [==============================] - 0s 18ms/step - loss: 0.2708 - accuracy: 0.9121 - val_loss: 0.4475 - val_accuracy: 0.8462 Epoch 119/150 9/9 [==============================] - 0s 14ms/step - loss: 0.2679 - accuracy: 0.9069 - val_loss: 0.4540 - val_accuracy: 0.8462 Epoch 120/150 9/9 [==============================] - 0s 17ms/step - loss: 0.2726 - accuracy: 0.9016 - val_loss: 0.4533 - val_accuracy: 0.8462 Epoch 121/150 9/9 [==============================] - 0s 15ms/step - loss: 0.2736 - accuracy: 0.9069 - val_loss: 0.4517 - val_accuracy: 0.8392 Epoch 122/150 9/9 [==============================] - 0s 16ms/step - loss: 0.2670 - accuracy: 0.9033 - val_loss: 0.4586 - val_accuracy: 0.8462 Epoch 123/150 9/9 [==============================] - 0s 15ms/step - loss: 0.2681 - accuracy: 0.9051 - val_loss: 0.4560 - val_accuracy: 0.8392 Epoch 124/150 9/9 [==============================] - 0s 15ms/step - loss: 0.2680 - accuracy: 0.9104 - val_loss: 0.4657 - val_accuracy: 0.8392 Epoch 125/150 9/9 [==============================] - 0s 16ms/step - loss: 0.2647 - accuracy: 0.9121 - val_loss: 0.4604 - val_accuracy: 0.8392 Epoch 126/150 9/9 [==============================] - 0s 13ms/step - loss: 0.2639 - accuracy: 0.9104 - val_loss: 0.4591 - val_accuracy: 0.8392 Epoch 127/150 9/9 [==============================] - 0s 14ms/step - loss: 0.2632 - accuracy: 0.9086 - val_loss: 0.4615 - val_accuracy: 0.8392 Epoch 128/150 9/9 [==============================] - 0s 16ms/step - loss: 0.2664 - accuracy: 0.9104 - val_loss: 0.4614 - val_accuracy: 0.8392 Epoch 129/150 9/9 [==============================] - 0s 15ms/step - loss: 0.2601 - accuracy: 0.9121 - val_loss: 0.4698 - val_accuracy: 0.8392 Epoch 130/150 9/9 [==============================] - 0s 17ms/step - loss: 0.2615 - accuracy: 0.9121 - val_loss: 0.4667 - val_accuracy: 0.8392 Epoch 131/150 9/9 [==============================] - 0s 16ms/step - loss: 0.2621 - accuracy: 0.9121 - val_loss: 0.4665 - val_accuracy: 0.8392 Epoch 132/150 9/9 [==============================] - 0s 14ms/step - loss: 0.2646 - accuracy: 0.9016 - val_loss: 0.4735 - val_accuracy: 0.8462 Epoch 133/150 9/9 [==============================] - 0s 18ms/step - loss: 0.2636 - accuracy: 0.9104 - val_loss: 0.4720 - val_accuracy: 0.8392 Epoch 134/150 9/9 [==============================] - 0s 15ms/step - loss: 0.2622 - accuracy: 0.9069 - val_loss: 0.4792 - val_accuracy: 0.8392 Epoch 135/150 9/9 [==============================] - 0s 16ms/step - loss: 0.2576 - accuracy: 0.9104 - val_loss: 0.4735 - val_accuracy: 0.8392 Epoch 136/150 9/9 [==============================] - 0s 17ms/step - loss: 0.2594 - accuracy: 0.9104 - val_loss: 0.4764 - val_accuracy: 0.8392 Epoch 137/150 9/9 [==============================] - 0s 14ms/step - loss: 0.2570 - accuracy: 0.9086 - val_loss: 0.4808 - val_accuracy: 0.8392 Epoch 138/150 9/9 [==============================] - 0s 15ms/step - loss: 0.2560 - accuracy: 0.9121 - val_loss: 0.4794 - val_accuracy: 0.8392 Epoch 139/150 9/9 [==============================] - 0s 9ms/step - loss: 0.2554 - accuracy: 0.9121 - val_loss: 0.4871 - val_accuracy: 0.8392 Epoch 140/150 9/9 [==============================] - 0s 9ms/step - loss: 0.2569 - accuracy: 0.9121 - val_loss: 0.4829 - val_accuracy: 0.8392 Epoch 141/150 9/9 [==============================] - 0s 9ms/step - loss: 0.2563 - accuracy: 0.9086 - val_loss: 0.4843 - val_accuracy: 0.8392 Epoch 142/150 9/9 [==============================] - 0s 8ms/step - loss: 0.2517 - accuracy: 0.9139 - val_loss: 0.4870 - val_accuracy: 0.8392 Epoch 143/150 9/9 [==============================] - 0s 10ms/step - loss: 0.2543 - accuracy: 0.9086 - val_loss: 0.4905 - val_accuracy: 0.8392 Epoch 144/150 9/9 [==============================] - 0s 8ms/step - loss: 0.2526 - accuracy: 0.9104 - val_loss: 0.4916 - val_accuracy: 0.8392 Epoch 145/150 9/9 [==============================] - 0s 11ms/step - loss: 0.2517 - accuracy: 0.9121 - val_loss: 0.4927 - val_accuracy: 0.8392 Epoch 146/150 9/9 [==============================] - 0s 11ms/step - loss: 0.2515 - accuracy: 0.9139 - val_loss: 0.5003 - val_accuracy: 0.8392 Epoch 147/150 9/9 [==============================] - 0s 9ms/step - loss: 0.2490 - accuracy: 0.9139 - val_loss: 0.4959 - val_accuracy: 0.8322 Epoch 148/150 9/9 [==============================] - 0s 10ms/step - loss: 0.2522 - accuracy: 0.9069 - val_loss: 0.4989 - val_accuracy: 0.8392 Epoch 149/150 9/9 [==============================] - 0s 9ms/step - loss: 0.2520 - accuracy: 0.9121 - val_loss: 0.4988 - val_accuracy: 0.8392 Epoch 150/150 9/9 [==============================] - 0s 8ms/step - loss: 0.2484 - accuracy: 0.9121 - val_loss: 0.5047 - val_accuracy: 0.8392
#Plotting Train Loss vs Validation Loss
plt.plot(history_keras.history['loss'])
plt.plot(history_keras.history['val_loss'])
plt.title('model loss')
plt.ylabel('Loss')
plt.xlabel('Epoch')
plt.legend(['train', 'validation'], loc='upper left')
plt.show()
#Plotting Epoch vs accuracy
plt.plot(history_keras.history['accuracy'])
plt.plot(history_keras.history['val_accuracy'])
plt.title('Accuracy vs Epochs')
plt.ylabel('Accuracy')
plt.xlabel('Epoch')
plt.legend(['Train', 'Validation'], loc='lower right')
plt.show()
# Using the model to make predictions on the training data
y_train_pred = modelkeras.predict(x_train_scaled)
#Set the threshold of classification to be 0.5
y_train_pred = (y_train_pred > 0.5)
#Performance of model on training data
metrics_score(y_train,y_train_pred)
23/23 [==============================] - 0s 2ms/step
precision recall f1-score support
0 0.89 0.95 0.92 443
1 0.91 0.81 0.86 269
accuracy 0.90 712
macro avg 0.90 0.88 0.89 712
weighted avg 0.90 0.90 0.90 712
#Making prediction using the model on the validation data to peformance metric.
y_pred=modelkeras.predict(x_test_scaled)
#Set the threshold of classification to be 0.5
y_pred = (y_pred > 0.5)
#Performance on validation data
metrics_score(y_test,y_pred)
6/6 [==============================] - 0s 3ms/step
precision recall f1-score support
0 0.76 0.84 0.80 106
1 0.73 0.62 0.67 73
accuracy 0.75 179
macro avg 0.74 0.73 0.73 179
weighted avg 0.75 0.75 0.74 179
# predict probabilities
yhatkeras = modelkeras.predict(x_test_scaled)
# keep probabilities for the positive outcome only
yhatkeras = yhatkeras[:, 0]
# calculate roc curves
fpr, tpr, thresholdskeras = roc_curve(y_test, yhatkeras)
# calculate the g-mean for each threshold
gmeanskeras = np.sqrt(tpr * (1-fpr))
# locate the index of the largest g-mean
ix = np.argmax(gmeanskeras)
print('Best Threshold=%f, G-Mean=%.3f' % (thresholdskeras[ix], gmeanskeras[ix]))
# plot the roc curve for the model
pyplot.plot([0,1], [0,1], linestyle='--', label='No Skill')
pyplot.plot(fpr, tpr, marker='.')
pyplot.scatter(fpr[ix], tpr[ix], marker='o', color='black', label='Best')
# axis labels
pyplot.xlabel('False Positive Rate')
pyplot.ylabel('True Positive Rate')
pyplot.legend()
# show the plot
pyplot.show()
6/6 [==============================] - 0s 2ms/step Best Threshold=0.150022, G-Mean=0.769
#Making the prediction using the test data
y_pred_e4=modelkeras.predict(x_test_scaled)
#Using the threshold value to convert the predicted data into true or false statements. If the predicted data is higher than threshold, it will be labelled true.
y_pred_e4 = (y_pred_e4 > thresholdskeras[ix])
metrics_score(y_test, y_pred_e4)
6/6 [==============================] - 0s 3ms/step
precision recall f1-score support
0 0.85 0.71 0.77 106
1 0.66 0.82 0.73 73
accuracy 0.75 179
macro avg 0.76 0.76 0.75 179
weighted avg 0.77 0.75 0.76 179
Recommendations:
# Try below code to install dask in Google Colab
!pip install dask-ml
Collecting dask-ml
Downloading dask_ml-2024.4.4-py3-none-any.whl (149 kB)
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 149.8/149.8 kB 2.9 MB/s eta 0:00:00
Collecting dask-glm>=0.2.0 (from dask-ml)
Downloading dask_glm-0.3.2-py2.py3-none-any.whl (13 kB)
Requirement already satisfied: dask[array,dataframe]>=2.4.0 in /usr/local/lib/python3.10/dist-packages (from dask-ml) (2023.8.1)
Requirement already satisfied: distributed>=2.4.0 in /usr/local/lib/python3.10/dist-packages (from dask-ml) (2023.8.1)
Requirement already satisfied: multipledispatch>=0.4.9 in /usr/local/lib/python3.10/dist-packages (from dask-ml) (1.0.0)
Requirement already satisfied: numba>=0.51.0 in /usr/local/lib/python3.10/dist-packages (from dask-ml) (0.58.1)
Requirement already satisfied: numpy>=1.20.0 in /usr/local/lib/python3.10/dist-packages (from dask-ml) (1.23.5)
Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from dask-ml) (24.0)
Requirement already satisfied: pandas>=0.24.2 in /usr/local/lib/python3.10/dist-packages (from dask-ml) (2.0.3)
Requirement already satisfied: scikit-learn>=1.2.0 in /usr/local/lib/python3.10/dist-packages (from dask-ml) (1.2.2)
Requirement already satisfied: scipy in /usr/local/lib/python3.10/dist-packages (from dask-ml) (1.11.4)
Requirement already satisfied: cloudpickle>=0.2.2 in /usr/local/lib/python3.10/dist-packages (from dask-glm>=0.2.0->dask-ml) (2.2.1)
Collecting sparse>=0.7.0 (from dask-glm>=0.2.0->dask-ml)
Downloading sparse-0.15.1-py2.py3-none-any.whl (116 kB)
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 116.3/116.3 kB 6.3 MB/s eta 0:00:00
Requirement already satisfied: click>=8.0 in /usr/local/lib/python3.10/dist-packages (from dask[array,dataframe]>=2.4.0->dask-ml) (8.1.7)
Requirement already satisfied: fsspec>=2021.09.0 in /usr/local/lib/python3.10/dist-packages (from dask[array,dataframe]>=2.4.0->dask-ml) (2023.6.0)
Requirement already satisfied: partd>=1.2.0 in /usr/local/lib/python3.10/dist-packages (from dask[array,dataframe]>=2.4.0->dask-ml) (1.4.1)
Requirement already satisfied: pyyaml>=5.3.1 in /usr/local/lib/python3.10/dist-packages (from dask[array,dataframe]>=2.4.0->dask-ml) (6.0.1)
Requirement already satisfied: toolz>=0.10.0 in /usr/local/lib/python3.10/dist-packages (from dask[array,dataframe]>=2.4.0->dask-ml) (0.12.1)
Requirement already satisfied: importlib-metadata>=4.13.0 in /usr/local/lib/python3.10/dist-packages (from dask[array,dataframe]>=2.4.0->dask-ml) (7.1.0)
Requirement already satisfied: jinja2>=2.10.3 in /usr/local/lib/python3.10/dist-packages (from distributed>=2.4.0->dask-ml) (3.1.3)
Requirement already satisfied: locket>=1.0.0 in /usr/local/lib/python3.10/dist-packages (from distributed>=2.4.0->dask-ml) (1.0.0)
Requirement already satisfied: msgpack>=1.0.0 in /usr/local/lib/python3.10/dist-packages (from distributed>=2.4.0->dask-ml) (1.0.8)
Requirement already satisfied: psutil>=5.7.2 in /usr/local/lib/python3.10/dist-packages (from distributed>=2.4.0->dask-ml) (5.9.5)
Requirement already satisfied: sortedcontainers>=2.0.5 in /usr/local/lib/python3.10/dist-packages (from distributed>=2.4.0->dask-ml) (2.4.0)
Requirement already satisfied: tblib>=1.6.0 in /usr/local/lib/python3.10/dist-packages (from distributed>=2.4.0->dask-ml) (3.0.0)
Requirement already satisfied: tornado>=6.0.4 in /usr/local/lib/python3.10/dist-packages (from distributed>=2.4.0->dask-ml) (6.3.3)
Requirement already satisfied: urllib3>=1.24.3 in /usr/local/lib/python3.10/dist-packages (from distributed>=2.4.0->dask-ml) (2.0.7)
Requirement already satisfied: zict>=2.2.0 in /usr/local/lib/python3.10/dist-packages (from distributed>=2.4.0->dask-ml) (3.0.0)
Requirement already satisfied: llvmlite<0.42,>=0.41.0dev0 in /usr/local/lib/python3.10/dist-packages (from numba>=0.51.0->dask-ml) (0.41.1)
Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.10/dist-packages (from pandas>=0.24.2->dask-ml) (2.8.2)
Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=0.24.2->dask-ml) (2023.4)
Requirement already satisfied: tzdata>=2022.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=0.24.2->dask-ml) (2024.1)
Requirement already satisfied: joblib>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from scikit-learn>=1.2.0->dask-ml) (1.4.0)
Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn>=1.2.0->dask-ml) (3.4.0)
Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.10/dist-packages (from importlib-metadata>=4.13.0->dask[array,dataframe]>=2.4.0->dask-ml) (3.18.1)
Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2>=2.10.3->distributed>=2.4.0->dask-ml) (2.1.5)
Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.2->pandas>=0.24.2->dask-ml) (1.16.0)
Installing collected packages: sparse, dask-glm, dask-ml
Successfully installed dask-glm-0.3.2 dask-ml-2024.4.4 sparse-0.15.1
# importing library
from dask_ml.model_selection import GridSearchCV as DaskGridSearchCV
def create_model(lr,batch_size,dropout1,dropout2):
# Fixing the seed for random number generators
np.random.seed(42)
# Initialize sequential model
model = Sequential()
model.add(Dense(64, activation='leaky_relu', input_dim = x_train.shape[1])) # Add the input layer and the first layer
model.add(Dropout(dropout1))
model.add(BatchNormalization())
model.add(Dense(64,activation='leaky_relu'))
model.add(Dropout(dropout2))
model.add(BatchNormalization())
# model.add(Dense(64,activation='leaky_relu'))
# model.add(Dropout(dropout))
# model.add(BatchNormalization())
# model.add(Dense(32,activation='leaky_relu'))
# model.add(Dropout(dropout))
# model.add(BatchNormalization())
model.add(Dense(1, activation='sigmoid'))
#Defining the optimizer and learnign rate
optimizer = Adamax(learning_rate = lr)
#Using the settings for the sequential model above, create the model with the following algorithms
model.compile(loss = 'binary_crossentropy',
optimizer = optimizer,
metrics=['accuracy'])
return model
#Reset the session
backend.clear_session()
np.random.seed(42)
random.seed(42)
tf.random.set_seed(42)
# define the grid search parameters
param_dask = {'batch_size':[32, 64, 128],
"lr":[0.0003, 0.0005, 0.0007, 0.001],
'dropout1':[0.1, 0.2, 0.3],
'dropout2':[0.1, 0.2, 0.3]}
#Create the classifer
keras_estimator = KerasClassifier(build_fn=create_model,
epochs = 150,
batch_size = 0,
verbose=1)
#Dask Tuner setting
kfold_splits = 3
dask = DaskGridSearchCV(estimator=keras_estimator,
cv=kfold_splits,
param_grid=param_dask,
n_jobs=-1)
import time
# store starting time
begin = time.time()
#Fit the optimizer
dask_result = dask.fit(x_train_scaled, y_train,validation_split=0.2,verbose=1)
# Summarize results
print("Best: %f using %s" % (dask_result.best_score_, dask_result.best_params_))
# store end time (Took almost 3 hours to run during tests)
time.sleep(1)
end = time.time()
# total time taken
print(f"Total runtime of the program is {end - begin}")
Streaming output truncated to the last 5000 lines.
Epoch 133/150
3/3 [==============================] - 0s 76ms/step - loss: 0.4080 - accuracy: 0.8132 - val_loss: 0.3746 - val_accuracy: 0.8737
Epoch 79/150
3/3 [==============================] - 0s 85ms/step - loss: 0.3995 - accuracy: 0.8526 - val_loss: 0.4688 - val_accuracy: 0.7789
Epoch 134/150
3/3 [==============================] - 0s 68ms/step - loss: 0.4405 - accuracy: 0.7921 - val_loss: 0.3742 - val_accuracy: 0.8737
Epoch 80/150
3/3 [==============================] - 0s 77ms/step - loss: 0.3744 - accuracy: 0.8500 - val_loss: 0.4693 - val_accuracy: 0.7789
Epoch 135/150
3/3 [==============================] - 0s 72ms/step - loss: 0.4017 - accuracy: 0.8132 - val_loss: 0.3737 - val_accuracy: 0.8737
Epoch 81/150
3/3 [==============================] - 0s 80ms/step - loss: 0.3978 - accuracy: 0.8526 - val_loss: 0.4697 - val_accuracy: 0.7789
Epoch 136/150
3/3 [==============================] - 0s 70ms/step - loss: 0.4068 - accuracy: 0.8184 - val_loss: 0.3732 - val_accuracy: 0.8737
Epoch 82/150
3/3 [==============================] - 0s 88ms/step - loss: 0.4205 - accuracy: 0.8026 - val_loss: 0.3723 - val_accuracy: 0.8737
Epoch 83/150
3/3 [==============================] - 0s 120ms/step - loss: 0.3973 - accuracy: 0.8500 - val_loss: 0.4693 - val_accuracy: 0.7895
Epoch 137/150
3/3 [==============================] - 0s 80ms/step - loss: 0.4157 - accuracy: 0.8237 - val_loss: 0.3714 - val_accuracy: 0.8737
Epoch 84/150
3/3 [==============================] - 0s 94ms/step - loss: 0.4093 - accuracy: 0.8395 - val_loss: 0.4688 - val_accuracy: 0.7895
Epoch 138/150
3/3 [==============================] - 0s 106ms/step - loss: 0.4015 - accuracy: 0.8421 - val_loss: 0.3704 - val_accuracy: 0.8737
Epoch 85/150
3/3 [==============================] - 0s 97ms/step - loss: 0.4146 - accuracy: 0.8368 - val_loss: 0.4681 - val_accuracy: 0.7895
Epoch 139/150
3/3 [==============================] - 0s 93ms/step - loss: 0.4225 - accuracy: 0.8026 - val_loss: 0.3694 - val_accuracy: 0.8737
Epoch 86/150
3/3 [==============================] - 0s 86ms/step - loss: 0.4122 - accuracy: 0.8263 - val_loss: 0.4680 - val_accuracy: 0.8000
Epoch 140/150
3/3 [==============================] - 0s 82ms/step - loss: 0.4258 - accuracy: 0.8211 - val_loss: 0.3680 - val_accuracy: 0.8737
Epoch 87/150
3/3 [==============================] - 0s 79ms/step - loss: 0.4041 - accuracy: 0.8474 - val_loss: 0.4681 - val_accuracy: 0.8000
Epoch 141/150
3/3 [==============================] - 0s 81ms/step - loss: 0.4317 - accuracy: 0.8053 - val_loss: 0.3668 - val_accuracy: 0.8737
Epoch 88/150
3/3 [==============================] - 0s 87ms/step - loss: 0.3939 - accuracy: 0.8316 - val_loss: 0.4681 - val_accuracy: 0.8000
Epoch 142/150
3/3 [==============================] - 0s 78ms/step - loss: 0.4399 - accuracy: 0.8158 - val_loss: 0.3659 - val_accuracy: 0.8737
Epoch 89/150
3/3 [==============================] - 0s 87ms/step - loss: 0.3867 - accuracy: 0.8368 - val_loss: 0.4685 - val_accuracy: 0.8000
Epoch 143/150
3/3 [==============================] - 0s 83ms/step - loss: 0.3926 - accuracy: 0.8158 - val_loss: 0.3652 - val_accuracy: 0.8737
Epoch 90/150
3/3 [==============================] - 0s 91ms/step - loss: 0.4037 - accuracy: 0.8316 - val_loss: 0.4685 - val_accuracy: 0.8000
Epoch 144/150
3/3 [==============================] - 0s 75ms/step - loss: 0.4258 - accuracy: 0.8211 - val_loss: 0.3646 - val_accuracy: 0.8737
Epoch 91/150
3/3 [==============================] - 0s 79ms/step - loss: 0.3781 - accuracy: 0.8421 - val_loss: 0.4686 - val_accuracy: 0.8000
Epoch 145/150
3/3 [==============================] - 0s 81ms/step - loss: 0.4165 - accuracy: 0.8053 - val_loss: 0.3638 - val_accuracy: 0.8737
Epoch 92/150
3/3 [==============================] - 0s 87ms/step - loss: 0.4175 - accuracy: 0.8211 - val_loss: 0.4686 - val_accuracy: 0.8000
Epoch 146/150
3/3 [==============================] - 0s 81ms/step - loss: 0.4305 - accuracy: 0.8184 - val_loss: 0.3632 - val_accuracy: 0.8737
Epoch 93/150
3/3 [==============================] - 0s 82ms/step - loss: 0.3944 - accuracy: 0.8395 - val_loss: 0.4687 - val_accuracy: 0.8000
Epoch 147/150
3/3 [==============================] - 0s 75ms/step - loss: 0.4140 - accuracy: 0.8184 - val_loss: 0.3626 - val_accuracy: 0.8737
Epoch 94/150
3/3 [==============================] - 0s 48ms/step - loss: 0.4412 - accuracy: 0.7921 - val_loss: 0.3627 - val_accuracy: 0.8737
Epoch 95/150
3/3 [==============================] - 0s 85ms/step - loss: 0.3849 - accuracy: 0.8342 - val_loss: 0.4687 - val_accuracy: 0.8000
Epoch 148/150
3/3 [==============================] - 0s 80ms/step - loss: 0.3919 - accuracy: 0.8263 - val_loss: 0.3629 - val_accuracy: 0.8737
Epoch 96/150
3/3 [==============================] - 0s 84ms/step - loss: 0.3900 - accuracy: 0.8342 - val_loss: 0.4688 - val_accuracy: 0.8000
1/3 [=========>....................] - ETA: 0s - loss: 0.3835 - accuracy: 0.8438Epoch 149/150
3/3 [==============================] - 0s 72ms/step - loss: 0.4388 - accuracy: 0.8000 - val_loss: 0.3628 - val_accuracy: 0.8737
Epoch 97/150
3/3 [==============================] - 0s 78ms/step - loss: 0.4188 - accuracy: 0.8368 - val_loss: 0.4688 - val_accuracy: 0.8000
Epoch 150/150
3/3 [==============================] - 0s 70ms/step - loss: 0.4355 - accuracy: 0.8053 - val_loss: 0.3628 - val_accuracy: 0.8737
Epoch 98/150
3/3 [==============================] - 0s 72ms/step - loss: 0.4006 - accuracy: 0.8316 - val_loss: 0.4690 - val_accuracy: 0.8000
3/3 [==============================] - 0s 47ms/step - loss: 0.4235 - accuracy: 0.8079 - val_loss: 0.3632 - val_accuracy: 0.8737
Epoch 99/150
3/3 [==============================] - 0s 43ms/step - loss: 0.4290 - accuracy: 0.8026 - val_loss: 0.3629 - val_accuracy: 0.8737
Epoch 100/150
3/3 [==============================] - 0s 64ms/step - loss: 0.4065 - accuracy: 0.8184 - val_loss: 0.3632 - val_accuracy: 0.8737
Epoch 101/150
3/3 [==============================] - 0s 51ms/step - loss: 0.4200 - accuracy: 0.8158 - val_loss: 0.3634 - val_accuracy: 0.8737
Epoch 102/150
3/3 [==============================] - 0s 42ms/step - loss: 0.4361 - accuracy: 0.8105 - val_loss: 0.3634 - val_accuracy: 0.8737
Epoch 103/150
3/3 [==============================] - 0s 53ms/step - loss: 0.4557 - accuracy: 0.7947 - val_loss: 0.3633 - val_accuracy: 0.8737
Epoch 104/150
3/3 [==============================] - 0s 44ms/step - loss: 0.4422 - accuracy: 0.8000 - val_loss: 0.3630 - val_accuracy: 0.8737
Epoch 105/150
3/3 [==============================] - 0s 51ms/step - loss: 0.4403 - accuracy: 0.8079 - val_loss: 0.3628 - val_accuracy: 0.8737
Epoch 106/150
3/3 [==============================] - 0s 46ms/step - loss: 0.3863 - accuracy: 0.8237 - val_loss: 0.3625 - val_accuracy: 0.8737
Epoch 107/150
3/3 [==============================] - 0s 48ms/step - loss: 0.4653 - accuracy: 0.7947 - val_loss: 0.3618 - val_accuracy: 0.8737
Epoch 108/150
3/3 [==============================] - 0s 47ms/step - loss: 0.4272 - accuracy: 0.8026 - val_loss: 0.3615 - val_accuracy: 0.8737
Epoch 109/150
3/3 [==============================] - 0s 50ms/step - loss: 0.3943 - accuracy: 0.8289 - val_loss: 0.3610 - val_accuracy: 0.8737
Epoch 110/150
3/3 [==============================] - 0s 52ms/step - loss: 0.4233 - accuracy: 0.7974 - val_loss: 0.3608 - val_accuracy: 0.8737
Epoch 111/150
3/3 [==============================] - 0s 51ms/step - loss: 0.4121 - accuracy: 0.8105 - val_loss: 0.3604 - val_accuracy: 0.8737
Epoch 112/150
3/3 [==============================] - 0s 52ms/step - loss: 0.4320 - accuracy: 0.8053 - val_loss: 0.3598 - val_accuracy: 0.8737
Epoch 113/150
3/3 [==============================] - 0s 53ms/step - loss: 0.4183 - accuracy: 0.8026 - val_loss: 0.3596 - val_accuracy: 0.8737
Epoch 114/150
3/3 [==============================] - 0s 54ms/step - loss: 0.4161 - accuracy: 0.8158 - val_loss: 0.3597 - val_accuracy: 0.8737
Epoch 115/150
3/3 [==============================] - 0s 64ms/step - loss: 0.4258 - accuracy: 0.7921 - val_loss: 0.3592 - val_accuracy: 0.8737
Epoch 116/150
3/3 [==============================] - 0s 45ms/step - loss: 0.4102 - accuracy: 0.8289 - val_loss: 0.3587 - val_accuracy: 0.8737
Epoch 117/150
3/3 [==============================] - 0s 49ms/step - loss: 0.3964 - accuracy: 0.8158 - val_loss: 0.3582 - val_accuracy: 0.8737
Epoch 118/150
3/3 [==============================] - 0s 51ms/step - loss: 0.4074 - accuracy: 0.8211 - val_loss: 0.3576 - val_accuracy: 0.8632
Epoch 119/150
3/3 [==============================] - 0s 56ms/step - loss: 0.4152 - accuracy: 0.8132 - val_loss: 0.3572 - val_accuracy: 0.8632
Epoch 120/150
3/3 [==============================] - 0s 55ms/step - loss: 0.4286 - accuracy: 0.8237 - val_loss: 0.3571 - val_accuracy: 0.8632
Epoch 121/150
3/3 [==============================] - 0s 53ms/step - loss: 0.4343 - accuracy: 0.8105 - val_loss: 0.3566 - val_accuracy: 0.8632
Epoch 122/150
3/3 [==============================] - 0s 43ms/step - loss: 0.4070 - accuracy: 0.8211 - val_loss: 0.3561 - val_accuracy: 0.8632
Epoch 123/150
3/3 [==============================] - 0s 55ms/step - loss: 0.4416 - accuracy: 0.8026 - val_loss: 0.3557 - val_accuracy: 0.8632
Epoch 124/150
3/3 [==============================] - 0s 47ms/step - loss: 0.4200 - accuracy: 0.8000 - val_loss: 0.3556 - val_accuracy: 0.8632
Epoch 125/150
3/3 [==============================] - 0s 53ms/step - loss: 0.4121 - accuracy: 0.8184 - val_loss: 0.3556 - val_accuracy: 0.8632
Epoch 126/150
3/3 [==============================] - 0s 53ms/step - loss: 0.4105 - accuracy: 0.8158 - val_loss: 0.3555 - val_accuracy: 0.8632
Epoch 127/150
3/3 [==============================] - 0s 43ms/step - loss: 0.4006 - accuracy: 0.8158 - val_loss: 0.3555 - val_accuracy: 0.8632
Epoch 128/150
3/3 [==============================] - 0s 50ms/step - loss: 0.3928 - accuracy: 0.8316 - val_loss: 0.3557 - val_accuracy: 0.8632
Epoch 129/150
3/3 [==============================] - 0s 56ms/step - loss: 0.4164 - accuracy: 0.7921 - val_loss: 0.3562 - val_accuracy: 0.8737
Epoch 130/150
3/3 [==============================] - 0s 52ms/step - loss: 0.3995 - accuracy: 0.8132 - val_loss: 0.3565 - val_accuracy: 0.8737
Epoch 131/150
3/3 [==============================] - 0s 53ms/step - loss: 0.4169 - accuracy: 0.8026 - val_loss: 0.3567 - val_accuracy: 0.8737
Epoch 132/150
3/3 [==============================] - 0s 41ms/step - loss: 0.4166 - accuracy: 0.8158 - val_loss: 0.3571 - val_accuracy: 0.8737
Epoch 133/150
3/3 [==============================] - 0s 40ms/step - loss: 0.4223 - accuracy: 0.8026 - val_loss: 0.3573 - val_accuracy: 0.8737
Epoch 134/150
3/3 [==============================] - 0s 37ms/step - loss: 0.4132 - accuracy: 0.8132 - val_loss: 0.3577 - val_accuracy: 0.8737
Epoch 135/150
3/3 [==============================] - 0s 62ms/step - loss: 0.3973 - accuracy: 0.8184 - val_loss: 0.3584 - val_accuracy: 0.8737
Epoch 136/150
3/3 [==============================] - 0s 60ms/step - loss: 0.3958 - accuracy: 0.8105 - val_loss: 0.3585 - val_accuracy: 0.8737
Epoch 137/150
3/3 [==============================] - 0s 58ms/step - loss: 0.3998 - accuracy: 0.8184 - val_loss: 0.3584 - val_accuracy: 0.8737
Epoch 138/150
3/3 [==============================] - 0s 46ms/step - loss: 0.3945 - accuracy: 0.8342 - val_loss: 0.3580 - val_accuracy: 0.8737
Epoch 139/150
3/3 [==============================] - 0s 52ms/step - loss: 0.4094 - accuracy: 0.8105 - val_loss: 0.3578 - val_accuracy: 0.8737
Epoch 140/150
3/3 [==============================] - 0s 52ms/step - loss: 0.4060 - accuracy: 0.8053 - val_loss: 0.3575 - val_accuracy: 0.8737
Epoch 141/150
3/3 [==============================] - 0s 74ms/step - loss: 0.4500 - accuracy: 0.7842 - val_loss: 0.3569 - val_accuracy: 0.8737
Epoch 142/150
3/3 [==============================] - 0s 62ms/step - loss: 0.4344 - accuracy: 0.8053 - val_loss: 0.3563 - val_accuracy: 0.8737
Epoch 143/150
3/3 [==============================] - 0s 65ms/step - loss: 0.3974 - accuracy: 0.8158 - val_loss: 0.3560 - val_accuracy: 0.8737
Epoch 144/150
3/3 [==============================] - 0s 61ms/step - loss: 0.3972 - accuracy: 0.8158 - val_loss: 0.3557 - val_accuracy: 0.8737
Epoch 145/150
3/3 [==============================] - 0s 62ms/step - loss: 0.4110 - accuracy: 0.8184 - val_loss: 0.3551 - val_accuracy: 0.8737
Epoch 146/150
3/3 [==============================] - 0s 49ms/step - loss: 0.4183 - accuracy: 0.7842 - val_loss: 0.3547 - val_accuracy: 0.8737
Epoch 147/150
3/3 [==============================] - 0s 61ms/step - loss: 0.4183 - accuracy: 0.7974 - val_loss: 0.3544 - val_accuracy: 0.8737
Epoch 148/150
3/3 [==============================] - 0s 50ms/step - loss: 0.4024 - accuracy: 0.8263 - val_loss: 0.3543 - val_accuracy: 0.8737
Epoch 149/150
3/3 [==============================] - 0s 54ms/step - loss: 0.4074 - accuracy: 0.8237 - val_loss: 0.3542 - val_accuracy: 0.8737
Epoch 150/150
3/3 [==============================] - 0s 47ms/step - loss: 0.3967 - accuracy: 0.8184 - val_loss: 0.3541 - val_accuracy: 0.8632
2/2 [==============================] - 0s 11ms/step - loss: 0.3886 - accuracy: 0.8439
Epoch 1/150
3/3 [==============================] - 3s 322ms/step - loss: 0.8915 - accuracy: 0.5145 - val_loss: 0.7237 - val_accuracy: 0.4842
Epoch 2/150
3/3 [==============================] - 0s 46ms/step - loss: 0.7334 - accuracy: 0.6385 - val_loss: 0.6962 - val_accuracy: 0.5368
Epoch 3/150
3/3 [==============================] - 0s 37ms/step - loss: 0.6804 - accuracy: 0.6359 - val_loss: 0.6718 - val_accuracy: 0.5895
Epoch 4/150
3/3 [==============================] - 0s 45ms/step - loss: 0.5987 - accuracy: 0.6834 - val_loss: 0.6521 - val_accuracy: 0.5789
Epoch 5/150
3/3 [==============================] - 0s 38ms/step - loss: 0.5487 - accuracy: 0.7335 - val_loss: 0.6349 - val_accuracy: 0.5895
Epoch 6/150
3/3 [==============================] - 0s 40ms/step - loss: 0.5347 - accuracy: 0.7414 - val_loss: 0.6190 - val_accuracy: 0.6316
Epoch 7/150
3/3 [==============================] - 0s 47ms/step - loss: 0.5056 - accuracy: 0.7599 - val_loss: 0.6049 - val_accuracy: 0.6632
Epoch 8/150
3/3 [==============================] - 0s 43ms/step - loss: 0.4715 - accuracy: 0.7916 - val_loss: 0.5920 - val_accuracy: 0.7158
Epoch 9/150
3/3 [==============================] - 0s 46ms/step - loss: 0.4727 - accuracy: 0.8047 - val_loss: 0.5806 - val_accuracy: 0.7263
Epoch 10/150
3/3 [==============================] - 0s 50ms/step - loss: 0.4845 - accuracy: 0.7678 - val_loss: 0.5704 - val_accuracy: 0.7474
Epoch 11/150
3/3 [==============================] - 0s 45ms/step - loss: 0.4780 - accuracy: 0.8127 - val_loss: 0.5623 - val_accuracy: 0.7579
Epoch 12/150
3/3 [==============================] - 0s 42ms/step - loss: 0.4419 - accuracy: 0.7995 - val_loss: 0.5542 - val_accuracy: 0.7895
Epoch 13/150
3/3 [==============================] - 0s 44ms/step - loss: 0.4382 - accuracy: 0.8206 - val_loss: 0.5474 - val_accuracy: 0.8000
Epoch 14/150
3/3 [==============================] - 0s 45ms/step - loss: 0.4265 - accuracy: 0.8047 - val_loss: 0.5410 - val_accuracy: 0.8000
Epoch 15/150
3/3 [==============================] - 0s 48ms/step - loss: 0.4053 - accuracy: 0.8179 - val_loss: 0.5353 - val_accuracy: 0.8000
Epoch 16/150
3/3 [==============================] - 0s 44ms/step - loss: 0.4477 - accuracy: 0.8021 - val_loss: 0.5299 - val_accuracy: 0.8000
Epoch 17/150
3/3 [==============================] - 0s 51ms/step - loss: 0.4151 - accuracy: 0.8206 - val_loss: 0.5245 - val_accuracy: 0.8000
Epoch 18/150
3/3 [==============================] - 0s 46ms/step - loss: 0.3893 - accuracy: 0.8338 - val_loss: 0.5197 - val_accuracy: 0.8000
Epoch 19/150
3/3 [==============================] - 0s 39ms/step - loss: 0.4087 - accuracy: 0.8311 - val_loss: 0.5155 - val_accuracy: 0.7895
Epoch 20/150
3/3 [==============================] - 0s 42ms/step - loss: 0.4517 - accuracy: 0.7863 - val_loss: 0.5113 - val_accuracy: 0.7895
Epoch 21/150
3/3 [==============================] - 0s 42ms/step - loss: 0.3774 - accuracy: 0.8470 - val_loss: 0.5069 - val_accuracy: 0.7895
Epoch 22/150
3/3 [==============================] - 0s 44ms/step - loss: 0.3943 - accuracy: 0.8417 - val_loss: 0.5027 - val_accuracy: 0.8000
Epoch 23/150
3/3 [==============================] - 0s 44ms/step - loss: 0.3820 - accuracy: 0.8628 - val_loss: 0.4987 - val_accuracy: 0.8000
Epoch 24/150
3/3 [==============================] - 0s 59ms/step - loss: 0.4123 - accuracy: 0.8153 - val_loss: 0.4952 - val_accuracy: 0.8000
Epoch 25/150
3/3 [==============================] - 0s 47ms/step - loss: 0.3946 - accuracy: 0.8259 - val_loss: 0.4920 - val_accuracy: 0.8000
Epoch 26/150
3/3 [==============================] - 0s 39ms/step - loss: 0.4105 - accuracy: 0.8338 - val_loss: 0.4890 - val_accuracy: 0.8000
Epoch 27/150
3/3 [==============================] - 0s 48ms/step - loss: 0.3900 - accuracy: 0.8259 - val_loss: 0.4863 - val_accuracy: 0.8000
Epoch 28/150
3/3 [==============================] - 0s 46ms/step - loss: 0.4000 - accuracy: 0.8232 - val_loss: 0.4839 - val_accuracy: 0.8000
Epoch 29/150
3/3 [==============================] - 0s 47ms/step - loss: 0.3931 - accuracy: 0.8259 - val_loss: 0.4819 - val_accuracy: 0.8105
Epoch 30/150
3/3 [==============================] - 0s 49ms/step - loss: 0.3826 - accuracy: 0.8522 - val_loss: 0.4797 - val_accuracy: 0.8105
Epoch 31/150
3/3 [==============================] - 0s 41ms/step - loss: 0.4024 - accuracy: 0.8338 - val_loss: 0.4781 - val_accuracy: 0.8105
Epoch 32/150
3/3 [==============================] - 0s 44ms/step - loss: 0.3935 - accuracy: 0.8443 - val_loss: 0.4763 - val_accuracy: 0.8105
Epoch 33/150
3/3 [==============================] - 0s 43ms/step - loss: 0.3858 - accuracy: 0.8496 - val_loss: 0.4747 - val_accuracy: 0.8105
Epoch 34/150
3/3 [==============================] - 0s 42ms/step - loss: 0.3958 - accuracy: 0.8100 - val_loss: 0.4731 - val_accuracy: 0.8211
Epoch 35/150
3/3 [==============================] - 0s 43ms/step - loss: 0.3623 - accuracy: 0.8496 - val_loss: 0.4717 - val_accuracy: 0.8211
Epoch 36/150
3/3 [==============================] - 0s 37ms/step - loss: 0.3778 - accuracy: 0.8549 - val_loss: 0.4706 - val_accuracy: 0.8211
Epoch 37/150
3/3 [==============================] - 0s 52ms/step - loss: 0.3906 - accuracy: 0.8443 - val_loss: 0.4692 - val_accuracy: 0.8211
Epoch 38/150
3/3 [==============================] - 0s 44ms/step - loss: 0.3863 - accuracy: 0.8311 - val_loss: 0.4680 - val_accuracy: 0.8211
Epoch 39/150
3/3 [==============================] - 0s 37ms/step - loss: 0.3751 - accuracy: 0.8470 - val_loss: 0.4665 - val_accuracy: 0.8211
Epoch 40/150
3/3 [==============================] - 0s 43ms/step - loss: 0.3947 - accuracy: 0.8364 - val_loss: 0.4654 - val_accuracy: 0.8211
Epoch 41/150
3/3 [==============================] - 0s 41ms/step - loss: 0.4063 - accuracy: 0.8417 - val_loss: 0.4647 - val_accuracy: 0.8211
Epoch 42/150
3/3 [==============================] - 0s 46ms/step - loss: 0.3748 - accuracy: 0.8707 - val_loss: 0.4637 - val_accuracy: 0.8211
Epoch 43/150
3/3 [==============================] - 0s 43ms/step - loss: 0.3470 - accuracy: 0.8496 - val_loss: 0.4624 - val_accuracy: 0.8316
Epoch 44/150
3/3 [==============================] - 0s 43ms/step - loss: 0.3736 - accuracy: 0.8417 - val_loss: 0.4611 - val_accuracy: 0.8316
Epoch 45/150
3/3 [==============================] - 0s 45ms/step - loss: 0.3899 - accuracy: 0.8259 - val_loss: 0.4597 - val_accuracy: 0.8316
Epoch 46/150
3/3 [==============================] - 0s 45ms/step - loss: 0.3561 - accuracy: 0.8417 - val_loss: 0.4583 - val_accuracy: 0.8316
Epoch 47/150
3/3 [==============================] - 0s 49ms/step - loss: 0.3582 - accuracy: 0.8602 - val_loss: 0.4573 - val_accuracy: 0.8316
Epoch 48/150
3/3 [==============================] - 0s 45ms/step - loss: 0.3614 - accuracy: 0.8391 - val_loss: 0.4559 - val_accuracy: 0.8316
Epoch 49/150
3/3 [==============================] - 0s 59ms/step - loss: 0.4034 - accuracy: 0.8179 - val_loss: 0.4550 - val_accuracy: 0.8316
Epoch 50/150
3/3 [==============================] - 0s 54ms/step - loss: 0.3566 - accuracy: 0.8470 - val_loss: 0.4542 - val_accuracy: 0.8316
Epoch 51/150
3/3 [==============================] - 0s 51ms/step - loss: 0.3946 - accuracy: 0.8391 - val_loss: 0.4537 - val_accuracy: 0.8316
Epoch 52/150
3/3 [==============================] - 0s 56ms/step - loss: 0.3671 - accuracy: 0.8417 - val_loss: 0.4529 - val_accuracy: 0.8316
Epoch 53/150
3/3 [==============================] - 0s 64ms/step - loss: 0.3580 - accuracy: 0.8364 - val_loss: 0.4523 - val_accuracy: 0.8316
Epoch 54/150
3/3 [==============================] - 0s 57ms/step - loss: 0.3798 - accuracy: 0.8391 - val_loss: 0.4521 - val_accuracy: 0.8316
Epoch 55/150
3/3 [==============================] - 0s 55ms/step - loss: 0.3598 - accuracy: 0.8654 - val_loss: 0.4519 - val_accuracy: 0.8316
Epoch 56/150
3/3 [==============================] - 0s 51ms/step - loss: 0.3414 - accuracy: 0.8443 - val_loss: 0.4516 - val_accuracy: 0.8316
Epoch 57/150
3/3 [==============================] - 0s 50ms/step - loss: 0.3549 - accuracy: 0.8654 - val_loss: 0.4512 - val_accuracy: 0.8316
Epoch 58/150
3/3 [==============================] - 0s 55ms/step - loss: 0.3510 - accuracy: 0.8417 - val_loss: 0.4510 - val_accuracy: 0.8316
Epoch 59/150
3/3 [==============================] - 0s 55ms/step - loss: 0.3546 - accuracy: 0.8602 - val_loss: 0.4508 - val_accuracy: 0.8316
Epoch 60/150
3/3 [==============================] - 0s 45ms/step - loss: 0.3630 - accuracy: 0.8496 - val_loss: 0.4510 - val_accuracy: 0.8316
Epoch 61/150
3/3 [==============================] - 0s 51ms/step - loss: 0.3518 - accuracy: 0.8549 - val_loss: 0.4511 - val_accuracy: 0.8316
Epoch 62/150
3/3 [==============================] - 0s 43ms/step - loss: 0.3571 - accuracy: 0.8338 - val_loss: 0.4513 - val_accuracy: 0.8316
Epoch 63/150
3/3 [==============================] - 0s 47ms/step - loss: 0.3495 - accuracy: 0.8575 - val_loss: 0.4512 - val_accuracy: 0.8316
Epoch 64/150
3/3 [==============================] - 0s 45ms/step - loss: 0.3668 - accuracy: 0.8575 - val_loss: 0.4516 - val_accuracy: 0.8316
Epoch 65/150
3/3 [==============================] - 0s 39ms/step - loss: 0.3592 - accuracy: 0.8522 - val_loss: 0.4522 - val_accuracy: 0.8316
Epoch 66/150
3/3 [==============================] - 0s 46ms/step - loss: 0.3487 - accuracy: 0.8522 - val_loss: 0.4525 - val_accuracy: 0.8316
Epoch 67/150
3/3 [==============================] - 0s 49ms/step - loss: 0.3273 - accuracy: 0.8654 - val_loss: 0.4527 - val_accuracy: 0.8316
Epoch 68/150
3/3 [==============================] - 0s 46ms/step - loss: 0.3470 - accuracy: 0.8654 - val_loss: 0.4526 - val_accuracy: 0.8316
Epoch 69/150
3/3 [==============================] - 0s 34ms/step - loss: 0.3571 - accuracy: 0.8654 - val_loss: 0.4525 - val_accuracy: 0.8316
Epoch 70/150
3/3 [==============================] - 0s 47ms/step - loss: 0.3705 - accuracy: 0.8575 - val_loss: 0.4524 - val_accuracy: 0.8316
Epoch 71/150
3/3 [==============================] - 0s 48ms/step - loss: 0.3542 - accuracy: 0.8417 - val_loss: 0.4520 - val_accuracy: 0.8316
Epoch 72/150
3/3 [==============================] - 0s 51ms/step - loss: 0.3396 - accuracy: 0.8760 - val_loss: 0.4519 - val_accuracy: 0.8316
Epoch 73/150
3/3 [==============================] - 0s 42ms/step - loss: 0.3770 - accuracy: 0.8417 - val_loss: 0.4516 - val_accuracy: 0.8316
Epoch 74/150
3/3 [==============================] - 0s 47ms/step - loss: 0.3466 - accuracy: 0.8522 - val_loss: 0.4515 - val_accuracy: 0.8211
Epoch 75/150
3/3 [==============================] - 0s 47ms/step - loss: 0.3392 - accuracy: 0.8522 - val_loss: 0.4520 - val_accuracy: 0.8211
Epoch 76/150
3/3 [==============================] - 0s 39ms/step - loss: 0.3594 - accuracy: 0.8575 - val_loss: 0.4531 - val_accuracy: 0.8211
Epoch 77/150
3/3 [==============================] - 0s 43ms/step - loss: 0.3335 - accuracy: 0.8602 - val_loss: 0.4539 - val_accuracy: 0.8211
Epoch 78/150
3/3 [==============================] - 0s 40ms/step - loss: 0.3129 - accuracy: 0.8681 - val_loss: 0.4545 - val_accuracy: 0.8316
Epoch 79/150
3/3 [==============================] - 0s 48ms/step - loss: 0.3447 - accuracy: 0.8549 - val_loss: 0.4550 - val_accuracy: 0.8211
Epoch 80/150
3/3 [==============================] - 0s 48ms/step - loss: 0.3451 - accuracy: 0.8575 - val_loss: 0.4557 - val_accuracy: 0.8211
Epoch 81/150
3/3 [==============================] - 0s 54ms/step - loss: 0.3423 - accuracy: 0.8549 - val_loss: 0.4562 - val_accuracy: 0.8211
Epoch 82/150
3/3 [==============================] - 0s 41ms/step - loss: 0.3262 - accuracy: 0.8628 - val_loss: 0.4567 - val_accuracy: 0.8211
Epoch 83/150
3/3 [==============================] - 0s 46ms/step - loss: 0.3364 - accuracy: 0.8522 - val_loss: 0.4570 - val_accuracy: 0.8211
Epoch 84/150
3/3 [==============================] - 0s 58ms/step - loss: 0.3350 - accuracy: 0.8681 - val_loss: 0.4574 - val_accuracy: 0.8211
Epoch 85/150
3/3 [==============================] - 0s 40ms/step - loss: 0.3693 - accuracy: 0.8391 - val_loss: 0.4580 - val_accuracy: 0.8211
Epoch 86/150
3/3 [==============================] - 0s 46ms/step - loss: 0.3548 - accuracy: 0.8522 - val_loss: 0.4590 - val_accuracy: 0.8211
Epoch 87/150
3/3 [==============================] - 0s 34ms/step - loss: 0.3471 - accuracy: 0.8470 - val_loss: 0.4604 - val_accuracy: 0.8211
Epoch 88/150
3/3 [==============================] - 0s 48ms/step - loss: 0.3784 - accuracy: 0.8470 - val_loss: 0.4616 - val_accuracy: 0.8211
Epoch 89/150
3/3 [==============================] - 0s 47ms/step - loss: 0.3501 - accuracy: 0.8522 - val_loss: 0.4626 - val_accuracy: 0.8211
Epoch 90/150
3/3 [==============================] - 0s 47ms/step - loss: 0.3576 - accuracy: 0.8628 - val_loss: 0.4633 - val_accuracy: 0.8211
Epoch 91/150
3/3 [==============================] - 0s 60ms/step - loss: 0.3337 - accuracy: 0.8602 - val_loss: 0.4638 - val_accuracy: 0.8211
Epoch 92/150
3/3 [==============================] - 0s 68ms/step - loss: 0.3530 - accuracy: 0.8417 - val_loss: 0.4639 - val_accuracy: 0.8211
Epoch 93/150
2/2 [==============================] - 0s 15ms/step - loss: 0.4166 - accuracy: 0.8270
3/3 [==============================] - 0s 87ms/step - loss: 0.3278 - accuracy: 0.8681 - val_loss: 0.4638 - val_accuracy: 0.8211
Epoch 94/150
3/3 [==============================] - 0s 86ms/step - loss: 0.3421 - accuracy: 0.8443 - val_loss: 0.4635 - val_accuracy: 0.8211
Epoch 95/150
3/3 [==============================] - 0s 91ms/step - loss: 0.3277 - accuracy: 0.8364 - val_loss: 0.4637 - val_accuracy: 0.8211
Epoch 96/150
3/3 [==============================] - 0s 60ms/step - loss: 0.3507 - accuracy: 0.8549 - val_loss: 0.4649 - val_accuracy: 0.8211
Epoch 97/150
3/3 [==============================] - 0s 76ms/step - loss: 0.3539 - accuracy: 0.8391 - val_loss: 0.4665 - val_accuracy: 0.8211
Epoch 98/150
1/3 [=========>....................] - ETA: 0s - loss: 0.2886 - accuracy: 0.8594Epoch 1/150
3/3 [==============================] - 0s 85ms/step - loss: 0.3312 - accuracy: 0.8654 - val_loss: 0.4677 - val_accuracy: 0.8211
Epoch 99/150
3/3 [==============================] - 0s 65ms/step - loss: 0.3388 - accuracy: 0.8575 - val_loss: 0.4688 - val_accuracy: 0.8211
Epoch 100/150
3/3 [==============================] - 0s 94ms/step - loss: 0.3158 - accuracy: 0.8786 - val_loss: 0.4699 - val_accuracy: 0.8211
Epoch 101/150
3/3 [==============================] - 0s 69ms/step - loss: 0.3470 - accuracy: 0.8707 - val_loss: 0.4710 - val_accuracy: 0.8211
Epoch 102/150
3/3 [==============================] - 0s 106ms/step - loss: 0.3145 - accuracy: 0.8734 - val_loss: 0.4719 - val_accuracy: 0.8211
Epoch 103/150
3/3 [==============================] - 0s 90ms/step - loss: 0.3268 - accuracy: 0.8602 - val_loss: 0.4728 - val_accuracy: 0.8211
Epoch 104/150
3/3 [==============================] - 0s 73ms/step - loss: 0.3448 - accuracy: 0.8575 - val_loss: 0.4737 - val_accuracy: 0.8211
Epoch 105/150
3/3 [==============================] - 0s 65ms/step - loss: 0.3316 - accuracy: 0.8628 - val_loss: 0.4744 - val_accuracy: 0.8211
Epoch 106/150
3/3 [==============================] - 0s 58ms/step - loss: 0.3471 - accuracy: 0.8575 - val_loss: 0.4755 - val_accuracy: 0.8211
Epoch 107/150
3/3 [==============================] - 0s 61ms/step - loss: 0.3306 - accuracy: 0.8734 - val_loss: 0.4764 - val_accuracy: 0.8211
Epoch 108/150
3/3 [==============================] - 0s 65ms/step - loss: 0.3372 - accuracy: 0.8602 - val_loss: 0.4770 - val_accuracy: 0.8211
Epoch 109/150
3/3 [==============================] - 0s 70ms/step - loss: 0.3203 - accuracy: 0.8760 - val_loss: 0.4769 - val_accuracy: 0.8211
Epoch 110/150
3/3 [==============================] - 0s 65ms/step - loss: 0.3302 - accuracy: 0.8707 - val_loss: 0.4772 - val_accuracy: 0.8211
Epoch 111/150
3/3 [==============================] - 0s 60ms/step - loss: 0.3600 - accuracy: 0.8522 - val_loss: 0.4777 - val_accuracy: 0.8211
Epoch 112/150
3/3 [==============================] - 0s 65ms/step - loss: 0.3549 - accuracy: 0.8681 - val_loss: 0.4784 - val_accuracy: 0.8211
Epoch 113/150
3/3 [==============================] - 0s 63ms/step - loss: 0.3169 - accuracy: 0.8760 - val_loss: 0.4793 - val_accuracy: 0.8316
Epoch 114/150
3/3 [==============================] - 0s 67ms/step - loss: 0.3259 - accuracy: 0.8681 - val_loss: 0.4797 - val_accuracy: 0.8316
Epoch 115/150
3/3 [==============================] - 0s 59ms/step - loss: 0.3354 - accuracy: 0.8575 - val_loss: 0.4803 - val_accuracy: 0.8316
Epoch 116/150
3/3 [==============================] - 0s 57ms/step - loss: 0.3204 - accuracy: 0.8786 - val_loss: 0.4805 - val_accuracy: 0.8211
Epoch 117/150
3/3 [==============================] - 0s 61ms/step - loss: 0.3254 - accuracy: 0.8760 - val_loss: 0.4805 - val_accuracy: 0.8105
Epoch 118/150
3/3 [==============================] - 0s 61ms/step - loss: 0.3393 - accuracy: 0.8681 - val_loss: 0.4806 - val_accuracy: 0.8105
Epoch 119/150
3/3 [==============================] - 0s 61ms/step - loss: 0.3399 - accuracy: 0.8681 - val_loss: 0.4810 - val_accuracy: 0.8105
Epoch 120/150
3/3 [==============================] - 0s 77ms/step - loss: 0.3389 - accuracy: 0.8760 - val_loss: 0.4808 - val_accuracy: 0.8105
Epoch 121/150
3/3 [==============================] - 0s 67ms/step - loss: 0.3367 - accuracy: 0.8602 - val_loss: 0.4811 - val_accuracy: 0.8105
Epoch 122/150
3/3 [==============================] - 0s 62ms/step - loss: 0.3154 - accuracy: 0.8707 - val_loss: 0.4817 - val_accuracy: 0.8105
Epoch 123/150
3/3 [==============================] - 0s 67ms/step - loss: 0.3295 - accuracy: 0.8654 - val_loss: 0.4829 - val_accuracy: 0.8105
Epoch 124/150
3/3 [==============================] - 0s 63ms/step - loss: 0.3621 - accuracy: 0.8549 - val_loss: 0.4841 - val_accuracy: 0.8105
Epoch 125/150
3/3 [==============================] - 0s 71ms/step - loss: 0.3271 - accuracy: 0.8681 - val_loss: 0.4850 - val_accuracy: 0.8105
Epoch 126/150
3/3 [==============================] - 0s 70ms/step - loss: 0.3291 - accuracy: 0.8681 - val_loss: 0.4863 - val_accuracy: 0.8105
Epoch 127/150
3/3 [==============================] - 0s 84ms/step - loss: 0.3398 - accuracy: 0.8654 - val_loss: 0.4873 - val_accuracy: 0.8105
Epoch 128/150
3/3 [==============================] - 0s 65ms/step - loss: 0.2952 - accuracy: 0.8813 - val_loss: 0.4875 - val_accuracy: 0.8105
Epoch 129/150
3/3 [==============================] - 0s 61ms/step - loss: 0.3391 - accuracy: 0.8602 - val_loss: 0.4879 - val_accuracy: 0.8000
Epoch 130/150
3/3 [==============================] - 0s 71ms/step - loss: 0.3231 - accuracy: 0.8654 - val_loss: 0.4886 - val_accuracy: 0.8000
Epoch 131/150
3/3 [==============================] - 0s 59ms/step - loss: 0.3372 - accuracy: 0.8575 - val_loss: 0.4896 - val_accuracy: 0.8000
Epoch 132/150
3/3 [==============================] - 0s 63ms/step - loss: 0.3437 - accuracy: 0.8522 - val_loss: 0.4904 - val_accuracy: 0.8000
Epoch 133/150
3/3 [==============================] - 6s 490ms/step - loss: 0.8618 - accuracy: 0.5132 - val_loss: 0.6711 - val_accuracy: 0.6105
Epoch 2/150
3/3 [==============================] - 0s 72ms/step - loss: 0.3296 - accuracy: 0.8602 - val_loss: 0.4916 - val_accuracy: 0.8105
Epoch 134/150
3/3 [==============================] - 0s 69ms/step - loss: 0.7542 - accuracy: 0.5868 - val_loss: 0.6461 - val_accuracy: 0.6947
Epoch 3/150
3/3 [==============================] - 0s 81ms/step - loss: 0.3279 - accuracy: 0.8865 - val_loss: 0.4925 - val_accuracy: 0.8105
Epoch 135/150
3/3 [==============================] - 0s 80ms/step - loss: 0.6411 - accuracy: 0.6974 - val_loss: 0.6278 - val_accuracy: 0.7263
Epoch 4/150
3/3 [==============================] - 0s 76ms/step - loss: 0.3072 - accuracy: 0.8760 - val_loss: 0.4935 - val_accuracy: 0.8105
Epoch 136/150
3/3 [==============================] - 0s 81ms/step - loss: 0.6087 - accuracy: 0.7000 - val_loss: 0.6127 - val_accuracy: 0.7368
Epoch 5/150
3/3 [==============================] - 0s 82ms/step - loss: 0.3570 - accuracy: 0.8575 - val_loss: 0.4943 - val_accuracy: 0.8105
Epoch 137/150
3/3 [==============================] - 0s 77ms/step - loss: 0.5867 - accuracy: 0.7158 - val_loss: 0.6005 - val_accuracy: 0.7474
Epoch 6/150
3/3 [==============================] - 0s 78ms/step - loss: 0.3136 - accuracy: 0.8734 - val_loss: 0.4951 - val_accuracy: 0.8105
Epoch 138/150
3/3 [==============================] - 0s 68ms/step - loss: 0.5654 - accuracy: 0.7447 - val_loss: 0.5902 - val_accuracy: 0.7368
Epoch 7/150
3/3 [==============================] - 0s 75ms/step - loss: 0.3278 - accuracy: 0.8734 - val_loss: 0.4965 - val_accuracy: 0.8105
Epoch 139/150
3/3 [==============================] - 0s 73ms/step - loss: 0.4827 - accuracy: 0.7868 - val_loss: 0.5811 - val_accuracy: 0.7368
Epoch 8/150
3/3 [==============================] - 0s 81ms/step - loss: 0.3182 - accuracy: 0.8734 - val_loss: 0.4969 - val_accuracy: 0.8105
Epoch 140/150
3/3 [==============================] - 0s 77ms/step - loss: 0.5473 - accuracy: 0.7526 - val_loss: 0.5741 - val_accuracy: 0.7368
Epoch 9/150
3/3 [==============================] - 0s 75ms/step - loss: 0.3179 - accuracy: 0.8707 - val_loss: 0.4976 - val_accuracy: 0.8105
Epoch 141/150
3/3 [==============================] - 0s 53ms/step - loss: 0.5120 - accuracy: 0.7816 - val_loss: 0.5681 - val_accuracy: 0.7579
Epoch 10/150
3/3 [==============================] - 0s 76ms/step - loss: 0.3097 - accuracy: 0.8734 - val_loss: 0.4976 - val_accuracy: 0.8000
Epoch 142/150
3/3 [==============================] - 0s 72ms/step - loss: 0.4957 - accuracy: 0.7711 - val_loss: 0.5623 - val_accuracy: 0.7684
Epoch 11/150
3/3 [==============================] - 0s 58ms/step - loss: 0.5226 - accuracy: 0.7368 - val_loss: 0.5567 - val_accuracy: 0.7474
Epoch 12/150
3/3 [==============================] - 0s 75ms/step - loss: 0.3318 - accuracy: 0.8760 - val_loss: 0.4980 - val_accuracy: 0.8000
Epoch 143/150
3/3 [==============================] - 0s 59ms/step - loss: 0.3366 - accuracy: 0.8654 - val_loss: 0.4983 - val_accuracy: 0.8000
Epoch 144/150
3/3 [==============================] - 0s 62ms/step - loss: 0.4688 - accuracy: 0.7895 - val_loss: 0.5522 - val_accuracy: 0.7579
Epoch 13/150
3/3 [==============================] - 0s 72ms/step - loss: 0.3262 - accuracy: 0.8628 - val_loss: 0.4986 - val_accuracy: 0.8000
Epoch 145/150
3/3 [==============================] - 0s 73ms/step - loss: 0.4991 - accuracy: 0.7816 - val_loss: 0.5479 - val_accuracy: 0.7579
Epoch 14/150
3/3 [==============================] - 0s 91ms/step - loss: 0.4722 - accuracy: 0.8079 - val_loss: 0.5437 - val_accuracy: 0.7579
3/3 [==============================] - 0s 85ms/step - loss: 0.3122 - accuracy: 0.8628 - val_loss: 0.4994 - val_accuracy: 0.8000
Epoch 146/150
Epoch 15/150
3/3 [==============================] - 0s 87ms/step - loss: 0.3466 - accuracy: 0.8549 - val_loss: 0.5006 - val_accuracy: 0.8000
Epoch 147/150
3/3 [==============================] - 0s 108ms/step - loss: 0.4868 - accuracy: 0.7974 - val_loss: 0.5396 - val_accuracy: 0.7579
Epoch 16/150
3/3 [==============================] - 0s 80ms/step - loss: 0.3192 - accuracy: 0.8654 - val_loss: 0.5018 - val_accuracy: 0.8000
Epoch 148/150
3/3 [==============================] - 0s 76ms/step - loss: 0.4391 - accuracy: 0.8263 - val_loss: 0.5361 - val_accuracy: 0.7579
Epoch 17/150
3/3 [==============================] - 0s 89ms/step - loss: 0.3191 - accuracy: 0.8602 - val_loss: 0.5030 - val_accuracy: 0.8000
Epoch 149/150
3/3 [==============================] - 0s 74ms/step - loss: 0.4930 - accuracy: 0.7816 - val_loss: 0.5324 - val_accuracy: 0.7684
Epoch 18/150
3/3 [==============================] - 0s 68ms/step - loss: 0.5199 - accuracy: 0.7579 - val_loss: 0.5300 - val_accuracy: 0.7684
Epoch 19/150
3/3 [==============================] - 0s 85ms/step - loss: 0.3246 - accuracy: 0.8628 - val_loss: 0.5041 - val_accuracy: 0.8000
Epoch 150/150
3/3 [==============================] - 0s 71ms/step - loss: 0.4721 - accuracy: 0.7895 - val_loss: 0.5275 - val_accuracy: 0.7684
Epoch 20/150
3/3 [==============================] - 0s 72ms/step - loss: 0.3206 - accuracy: 0.8602 - val_loss: 0.5047 - val_accuracy: 0.8000
3/3 [==============================] - 0s 75ms/step - loss: 0.4269 - accuracy: 0.8105 - val_loss: 0.5250 - val_accuracy: 0.7684
Epoch 21/150
3/3 [==============================] - 0s 69ms/step - loss: 0.4752 - accuracy: 0.7974 - val_loss: 0.5227 - val_accuracy: 0.7684
Epoch 22/150
2/2 [==============================] - 0s 9ms/step - loss: 0.5772 - accuracy: 0.7899
3/3 [==============================] - 0s 75ms/step - loss: 0.4530 - accuracy: 0.7895 - val_loss: 0.5200 - val_accuracy: 0.7789
Epoch 23/150
3/3 [==============================] - 0s 73ms/step - loss: 0.4545 - accuracy: 0.8026 - val_loss: 0.5173 - val_accuracy: 0.7789
Epoch 24/150
3/3 [==============================] - 0s 68ms/step - loss: 0.4739 - accuracy: 0.8053 - val_loss: 0.5151 - val_accuracy: 0.7789
Epoch 25/150
3/3 [==============================] - 0s 78ms/step - loss: 0.4634 - accuracy: 0.7842 - val_loss: 0.5126 - val_accuracy: 0.7789
Epoch 26/150
1/3 [=========>....................] - ETA: 0s - loss: 0.4765 - accuracy: 0.7812Epoch 1/150
3/3 [==============================] - 0s 68ms/step - loss: 0.4539 - accuracy: 0.7974 - val_loss: 0.5098 - val_accuracy: 0.7789
Epoch 27/150
3/3 [==============================] - 0s 76ms/step - loss: 0.4309 - accuracy: 0.7974 - val_loss: 0.5073 - val_accuracy: 0.7789
Epoch 28/150
3/3 [==============================] - 0s 81ms/step - loss: 0.4627 - accuracy: 0.8079 - val_loss: 0.5051 - val_accuracy: 0.7789
Epoch 29/150
3/3 [==============================] - 0s 89ms/step - loss: 0.4554 - accuracy: 0.7974 - val_loss: 0.5029 - val_accuracy: 0.7789
Epoch 30/150
3/3 [==============================] - 0s 80ms/step - loss: 0.4296 - accuracy: 0.8132 - val_loss: 0.5006 - val_accuracy: 0.7789
Epoch 31/150
3/3 [==============================] - 0s 63ms/step - loss: 0.4340 - accuracy: 0.8079 - val_loss: 0.4987 - val_accuracy: 0.7789
Epoch 32/150
3/3 [==============================] - 0s 81ms/step - loss: 0.4341 - accuracy: 0.8184 - val_loss: 0.4972 - val_accuracy: 0.7789
Epoch 33/150
3/3 [==============================] - 0s 67ms/step - loss: 0.4435 - accuracy: 0.8132 - val_loss: 0.4960 - val_accuracy: 0.7789
Epoch 34/150
3/3 [==============================] - 0s 73ms/step - loss: 0.4116 - accuracy: 0.8368 - val_loss: 0.4943 - val_accuracy: 0.7789
Epoch 35/150
3/3 [==============================] - 0s 76ms/step - loss: 0.4110 - accuracy: 0.8289 - val_loss: 0.4928 - val_accuracy: 0.7789
Epoch 36/150
3/3 [==============================] - 0s 83ms/step - loss: 0.4148 - accuracy: 0.8263 - val_loss: 0.4915 - val_accuracy: 0.7789
Epoch 37/150
3/3 [==============================] - 0s 69ms/step - loss: 0.4179 - accuracy: 0.8368 - val_loss: 0.4903 - val_accuracy: 0.7789
Epoch 38/150
3/3 [==============================] - 0s 69ms/step - loss: 0.4363 - accuracy: 0.8211 - val_loss: 0.4897 - val_accuracy: 0.7789
Epoch 39/150
3/3 [==============================] - 0s 75ms/step - loss: 0.4587 - accuracy: 0.8132 - val_loss: 0.4892 - val_accuracy: 0.7789
Epoch 40/150
3/3 [==============================] - 0s 74ms/step - loss: 0.4267 - accuracy: 0.8158 - val_loss: 0.4878 - val_accuracy: 0.7789
Epoch 41/150
3/3 [==============================] - 0s 72ms/step - loss: 0.4327 - accuracy: 0.8053 - val_loss: 0.4861 - val_accuracy: 0.7789
Epoch 42/150
3/3 [==============================] - 0s 77ms/step - loss: 0.4294 - accuracy: 0.8158 - val_loss: 0.4847 - val_accuracy: 0.7789
Epoch 43/150
3/3 [==============================] - 0s 70ms/step - loss: 0.4472 - accuracy: 0.7921 - val_loss: 0.4831 - val_accuracy: 0.7789
Epoch 44/150
3/3 [==============================] - 0s 67ms/step - loss: 0.4328 - accuracy: 0.8289 - val_loss: 0.4819 - val_accuracy: 0.7895
Epoch 45/150
3/3 [==============================] - 0s 75ms/step - loss: 0.4228 - accuracy: 0.8026 - val_loss: 0.4808 - val_accuracy: 0.7895
Epoch 46/150
3/3 [==============================] - 0s 78ms/step - loss: 0.4443 - accuracy: 0.8158 - val_loss: 0.4799 - val_accuracy: 0.7895
Epoch 47/150
3/3 [==============================] - 0s 78ms/step - loss: 0.4615 - accuracy: 0.8132 - val_loss: 0.4790 - val_accuracy: 0.7895
Epoch 48/150
3/3 [==============================] - 0s 75ms/step - loss: 0.4465 - accuracy: 0.8053 - val_loss: 0.4782 - val_accuracy: 0.7895
Epoch 49/150
3/3 [==============================] - 0s 70ms/step - loss: 0.4453 - accuracy: 0.8237 - val_loss: 0.4777 - val_accuracy: 0.7895
Epoch 50/150
3/3 [==============================] - 0s 78ms/step - loss: 0.4420 - accuracy: 0.8289 - val_loss: 0.4771 - val_accuracy: 0.7895
Epoch 51/150
3/3 [==============================] - 0s 63ms/step - loss: 0.4180 - accuracy: 0.8263 - val_loss: 0.4764 - val_accuracy: 0.7895
Epoch 52/150
3/3 [==============================] - 0s 89ms/step - loss: 0.4040 - accuracy: 0.8368 - val_loss: 0.4759 - val_accuracy: 0.7789
Epoch 53/150
3/3 [==============================] - 0s 103ms/step - loss: 0.4398 - accuracy: 0.8000 - val_loss: 0.4757 - val_accuracy: 0.7789
Epoch 54/150
3/3 [==============================] - 0s 80ms/step - loss: 0.4431 - accuracy: 0.8132 - val_loss: 0.4757 - val_accuracy: 0.7789
Epoch 55/150
3/3 [==============================] - 0s 98ms/step - loss: 0.4179 - accuracy: 0.8237 - val_loss: 0.4756 - val_accuracy: 0.7789
Epoch 56/150
3/3 [==============================] - 0s 76ms/step - loss: 0.4254 - accuracy: 0.8079 - val_loss: 0.4757 - val_accuracy: 0.7789
Epoch 57/150
3/3 [==============================] - 0s 68ms/step - loss: 0.4377 - accuracy: 0.8263 - val_loss: 0.4754 - val_accuracy: 0.7789
Epoch 58/150
3/3 [==============================] - 0s 63ms/step - loss: 0.3911 - accuracy: 0.8289 - val_loss: 0.4753 - val_accuracy: 0.7789
3/3 [==============================] - 6s 547ms/step - loss: 0.6741 - accuracy: 0.6237 - val_loss: 0.5993 - val_accuracy: 0.6842
Epoch 59/150
Epoch 2/150
3/3 [==============================] - 0s 80ms/step - loss: 0.6412 - accuracy: 0.6474 - val_loss: 0.5806 - val_accuracy: 0.7263
3/3 [==============================] - 0s 86ms/step - loss: 0.4233 - accuracy: 0.8316 - val_loss: 0.4752 - val_accuracy: 0.7789
Epoch 60/150
Epoch 3/150
3/3 [==============================] - 0s 63ms/step - loss: 0.6109 - accuracy: 0.6947 - val_loss: 0.5680 - val_accuracy: 0.7684
Epoch 4/150
3/3 [==============================] - 0s 96ms/step - loss: 0.4324 - accuracy: 0.8132 - val_loss: 0.4748 - val_accuracy: 0.7789
Epoch 61/150
3/3 [==============================] - 0s 76ms/step - loss: 0.5628 - accuracy: 0.7289 - val_loss: 0.5575 - val_accuracy: 0.7895
Epoch 5/150
3/3 [==============================] - 0s 90ms/step - loss: 0.3776 - accuracy: 0.8526 - val_loss: 0.4744 - val_accuracy: 0.7789
Epoch 62/150
3/3 [==============================] - 0s 96ms/step - loss: 0.6200 - accuracy: 0.7000 - val_loss: 0.5487 - val_accuracy: 0.8000
Epoch 6/150
3/3 [==============================] - 0s 106ms/step - loss: 0.4099 - accuracy: 0.8316 - val_loss: 0.4739 - val_accuracy: 0.7789
Epoch 63/150
3/3 [==============================] - 0s 111ms/step - loss: 0.5392 - accuracy: 0.7474 - val_loss: 0.5404 - val_accuracy: 0.8105
Epoch 7/150
3/3 [==============================] - 0s 113ms/step - loss: 0.4080 - accuracy: 0.8289 - val_loss: 0.4733 - val_accuracy: 0.7789
Epoch 64/150
3/3 [==============================] - 0s 83ms/step - loss: 0.5493 - accuracy: 0.7132 - val_loss: 0.5338 - val_accuracy: 0.8316
Epoch 8/150
3/3 [==============================] - 0s 83ms/step - loss: 0.4057 - accuracy: 0.8158 - val_loss: 0.4724 - val_accuracy: 0.7789
Epoch 65/150
3/3 [==============================] - 0s 76ms/step - loss: 0.4987 - accuracy: 0.7816 - val_loss: 0.5279 - val_accuracy: 0.8211
Epoch 9/150
3/3 [==============================] - 0s 92ms/step - loss: 0.4214 - accuracy: 0.8079 - val_loss: 0.4720 - val_accuracy: 0.7789
Epoch 66/150
3/3 [==============================] - 0s 87ms/step - loss: 0.5020 - accuracy: 0.7711 - val_loss: 0.5218 - val_accuracy: 0.8316
Epoch 10/150
3/3 [==============================] - 0s 74ms/step - loss: 0.4199 - accuracy: 0.8211 - val_loss: 0.4717 - val_accuracy: 0.7789
Epoch 67/150
3/3 [==============================] - 0s 87ms/step - loss: 0.5337 - accuracy: 0.7632 - val_loss: 0.5158 - val_accuracy: 0.8316
Epoch 11/150
3/3 [==============================] - 0s 90ms/step - loss: 0.4178 - accuracy: 0.8184 - val_loss: 0.4722 - val_accuracy: 0.7789
Epoch 68/150
3/3 [==============================] - 0s 76ms/step - loss: 0.4904 - accuracy: 0.7868 - val_loss: 0.5111 - val_accuracy: 0.8316
Epoch 12/150
3/3 [==============================] - 0s 92ms/step - loss: 0.4160 - accuracy: 0.8500 - val_loss: 0.4722 - val_accuracy: 0.7789
Epoch 69/150
3/3 [==============================] - 0s 84ms/step - loss: 0.5438 - accuracy: 0.7605 - val_loss: 0.5076 - val_accuracy: 0.8421
Epoch 13/150
3/3 [==============================] - 0s 123ms/step - loss: 0.4140 - accuracy: 0.8184 - val_loss: 0.4720 - val_accuracy: 0.7789
Epoch 70/150
3/3 [==============================] - 0s 123ms/step - loss: 0.5086 - accuracy: 0.7605 - val_loss: 0.5039 - val_accuracy: 0.8526
Epoch 14/150
3/3 [==============================] - 0s 102ms/step - loss: 0.4084 - accuracy: 0.8211 - val_loss: 0.4719 - val_accuracy: 0.7789
Epoch 71/150
3/3 [==============================] - 0s 110ms/step - loss: 0.5195 - accuracy: 0.7447 - val_loss: 0.5003 - val_accuracy: 0.8632
Epoch 15/150
3/3 [==============================] - 0s 62ms/step - loss: 0.4657 - accuracy: 0.8211 - val_loss: 0.4973 - val_accuracy: 0.8632
3/3 [==============================] - 0s 87ms/step - loss: 0.4012 - accuracy: 0.8342 - val_loss: 0.4721 - val_accuracy: 0.7789
Epoch 16/150
1/3 [=========>....................] - ETA: 0s - loss: 0.5101 - accuracy: 0.7422Epoch 72/150
3/3 [==============================] - 0s 100ms/step - loss: 0.4988 - accuracy: 0.7789 - val_loss: 0.4942 - val_accuracy: 0.8632
3/3 [==============================] - 0s 94ms/step - loss: 0.4298 - accuracy: 0.8263 - val_loss: 0.4722 - val_accuracy: 0.7789
Epoch 17/150
Epoch 73/150
3/3 [==============================] - 0s 82ms/step - loss: 0.4932 - accuracy: 0.7737 - val_loss: 0.4905 - val_accuracy: 0.8632
Epoch 18/150
3/3 [==============================] - 0s 95ms/step - loss: 0.3841 - accuracy: 0.8526 - val_loss: 0.4718 - val_accuracy: 0.7789
Epoch 74/150
3/3 [==============================] - 0s 72ms/step - loss: 0.4680 - accuracy: 0.7974 - val_loss: 0.4872 - val_accuracy: 0.8632
Epoch 19/150
3/3 [==============================] - 0s 66ms/step - loss: 0.4031 - accuracy: 0.8316 - val_loss: 0.4717 - val_accuracy: 0.7789
Epoch 75/150
3/3 [==============================] - 0s 67ms/step - loss: 0.4078 - accuracy: 0.8263 - val_loss: 0.4714 - val_accuracy: 0.7895
3/3 [==============================] - 0s 70ms/step - loss: 0.4500 - accuracy: 0.8079 - val_loss: 0.4840 - val_accuracy: 0.8632
Epoch 20/150
Epoch 76/150
3/3 [==============================] - 0s 70ms/step - loss: 0.5020 - accuracy: 0.7763 - val_loss: 0.4812 - val_accuracy: 0.8632
Epoch 21/150
3/3 [==============================] - 0s 74ms/step - loss: 0.4122 - accuracy: 0.8368 - val_loss: 0.4713 - val_accuracy: 0.7895
Epoch 77/150
3/3 [==============================] - 0s 66ms/step - loss: 0.5030 - accuracy: 0.7684 - val_loss: 0.4777 - val_accuracy: 0.8632
Epoch 22/150
3/3 [==============================] - 0s 85ms/step - loss: 0.3803 - accuracy: 0.8368 - val_loss: 0.4705 - val_accuracy: 0.7895
Epoch 78/150
3/3 [==============================] - 0s 56ms/step - loss: 0.4612 - accuracy: 0.7868 - val_loss: 0.4742 - val_accuracy: 0.8632
Epoch 23/150
3/3 [==============================] - 0s 78ms/step - loss: 0.3960 - accuracy: 0.8447 - val_loss: 0.4700 - val_accuracy: 0.7895
Epoch 79/150
3/3 [==============================] - 0s 69ms/step - loss: 0.4716 - accuracy: 0.8000 - val_loss: 0.4704 - val_accuracy: 0.8632
Epoch 24/150
3/3 [==============================] - 0s 77ms/step - loss: 0.3960 - accuracy: 0.8474 - val_loss: 0.4695 - val_accuracy: 0.7895
Epoch 80/150
3/3 [==============================] - 0s 71ms/step - loss: 0.4796 - accuracy: 0.8000 - val_loss: 0.4673 - val_accuracy: 0.8632
Epoch 25/150
3/3 [==============================] - 0s 75ms/step - loss: 0.3900 - accuracy: 0.8263 - val_loss: 0.4688 - val_accuracy: 0.7895
Epoch 81/150
3/3 [==============================] - 0s 73ms/step - loss: 0.4489 - accuracy: 0.8158 - val_loss: 0.4642 - val_accuracy: 0.8632
Epoch 26/150
3/3 [==============================] - 0s 88ms/step - loss: 0.3890 - accuracy: 0.8447 - val_loss: 0.4683 - val_accuracy: 0.7895
Epoch 82/150
3/3 [==============================] - 0s 75ms/step - loss: 0.5067 - accuracy: 0.7737 - val_loss: 0.4618 - val_accuracy: 0.8632
1/3 [=========>....................] - ETA: 0s - loss: 0.4387 - accuracy: 0.8125Epoch 27/150
3/3 [==============================] - 0s 72ms/step - loss: 0.3943 - accuracy: 0.8237 - val_loss: 0.4679 - val_accuracy: 0.7895
Epoch 83/150
3/3 [==============================] - 0s 83ms/step - loss: 0.4752 - accuracy: 0.7921 - val_loss: 0.4590 - val_accuracy: 0.8632
Epoch 28/150
3/3 [==============================] - 0s 52ms/step - loss: 0.4289 - accuracy: 0.8158 - val_loss: 0.4678 - val_accuracy: 0.7895
Epoch 84/150
3/3 [==============================] - 0s 85ms/step - loss: 0.4930 - accuracy: 0.7816 - val_loss: 0.4565 - val_accuracy: 0.8632
Epoch 29/150
3/3 [==============================] - 0s 87ms/step - loss: 0.3990 - accuracy: 0.8395 - val_loss: 0.4681 - val_accuracy: 0.7895
Epoch 85/150
3/3 [==============================] - 0s 97ms/step - loss: 0.4460 - accuracy: 0.8158 - val_loss: 0.4545 - val_accuracy: 0.8632
Epoch 30/150
3/3 [==============================] - 0s 85ms/step - loss: 0.3965 - accuracy: 0.8211 - val_loss: 0.4687 - val_accuracy: 0.7789
Epoch 86/150
3/3 [==============================] - 0s 94ms/step - loss: 0.4594 - accuracy: 0.8079 - val_loss: 0.4525 - val_accuracy: 0.8632
Epoch 31/150
3/3 [==============================] - 0s 77ms/step - loss: 0.4010 - accuracy: 0.8211 - val_loss: 0.4689 - val_accuracy: 0.7789
Epoch 87/150
3/3 [==============================] - 0s 96ms/step - loss: 0.4798 - accuracy: 0.8079 - val_loss: 0.4507 - val_accuracy: 0.8737
Epoch 32/150
3/3 [==============================] - 0s 73ms/step - loss: 0.4003 - accuracy: 0.8395 - val_loss: 0.4692 - val_accuracy: 0.7895
Epoch 88/150
3/3 [==============================] - 0s 78ms/step - loss: 0.4497 - accuracy: 0.7868 - val_loss: 0.4481 - val_accuracy: 0.8737
Epoch 33/150
3/3 [==============================] - 0s 95ms/step - loss: 0.3922 - accuracy: 0.8368 - val_loss: 0.4693 - val_accuracy: 0.7895
Epoch 89/150
3/3 [==============================] - 0s 85ms/step - loss: 0.4407 - accuracy: 0.8026 - val_loss: 0.4457 - val_accuracy: 0.8737
3/3 [==============================] - 0s 84ms/step - loss: 0.4111 - accuracy: 0.8184 - val_loss: 0.4699 - val_accuracy: 0.7789
Epoch 34/150
Epoch 90/150
3/3 [==============================] - 0s 73ms/step - loss: 0.4563 - accuracy: 0.8026 - val_loss: 0.4432 - val_accuracy: 0.8737
Epoch 35/150
3/3 [==============================] - 0s 102ms/step - loss: 0.3999 - accuracy: 0.8211 - val_loss: 0.4703 - val_accuracy: 0.7789
Epoch 91/150
3/3 [==============================] - 0s 67ms/step - loss: 0.4051 - accuracy: 0.8368 - val_loss: 0.4707 - val_accuracy: 0.7789
3/3 [==============================] - 0s 100ms/step - loss: 0.4431 - accuracy: 0.8184 - val_loss: 0.4415 - val_accuracy: 0.8737
Epoch 36/150
Epoch 92/150
3/3 [==============================] - 0s 73ms/step - loss: 0.3970 - accuracy: 0.8263 - val_loss: 0.4714 - val_accuracy: 0.7789
Epoch 93/150
3/3 [==============================] - 0s 109ms/step - loss: 0.4344 - accuracy: 0.8184 - val_loss: 0.4400 - val_accuracy: 0.8842
Epoch 37/150
3/3 [==============================] - 0s 82ms/step - loss: 0.3881 - accuracy: 0.8395 - val_loss: 0.4719 - val_accuracy: 0.7789
Epoch 94/150
3/3 [==============================] - 0s 82ms/step - loss: 0.4532 - accuracy: 0.8158 - val_loss: 0.4380 - val_accuracy: 0.8842
Epoch 38/150
3/3 [==============================] - 0s 92ms/step - loss: 0.3969 - accuracy: 0.8526 - val_loss: 0.4723 - val_accuracy: 0.7789
Epoch 95/150
3/3 [==============================] - 0s 89ms/step - loss: 0.4669 - accuracy: 0.7816 - val_loss: 0.4364 - val_accuracy: 0.8842
Epoch 39/150
3/3 [==============================] - 0s 106ms/step - loss: 0.4018 - accuracy: 0.8632 - val_loss: 0.4728 - val_accuracy: 0.7789
Epoch 96/150
3/3 [==============================] - 0s 77ms/step - loss: 0.4522 - accuracy: 0.8053 - val_loss: 0.4342 - val_accuracy: 0.8842
Epoch 40/150
3/3 [==============================] - 0s 64ms/step - loss: 0.4593 - accuracy: 0.8105 - val_loss: 0.4322 - val_accuracy: 0.8842
Epoch 41/150
3/3 [==============================] - 0s 80ms/step - loss: 0.3772 - accuracy: 0.8526 - val_loss: 0.4735 - val_accuracy: 0.7789
Epoch 97/150
3/3 [==============================] - 0s 72ms/step - loss: 0.4204 - accuracy: 0.8211 - val_loss: 0.4740 - val_accuracy: 0.7789
3/3 [==============================] - 0s 66ms/step - loss: 0.4842 - accuracy: 0.7763 - val_loss: 0.4310 - val_accuracy: 0.8842
Epoch 42/150
Epoch 98/150
3/3 [==============================] - 0s 72ms/step - loss: 0.4453 - accuracy: 0.7947 - val_loss: 0.4290 - val_accuracy: 0.8842
3/3 [==============================] - 0s 73ms/step - loss: 0.3836 - accuracy: 0.8553 - val_loss: 0.4746 - val_accuracy: 0.8000
Epoch 99/150
Epoch 43/150
3/3 [==============================] - 0s 72ms/step - loss: 0.4570 - accuracy: 0.7974 - val_loss: 0.4262 - val_accuracy: 0.8842
Epoch 44/150
3/3 [==============================] - 0s 81ms/step - loss: 0.3958 - accuracy: 0.8421 - val_loss: 0.4752 - val_accuracy: 0.8000
Epoch 100/150
3/3 [==============================] - 0s 88ms/step - loss: 0.4641 - accuracy: 0.8026 - val_loss: 0.4233 - val_accuracy: 0.8842
Epoch 45/150
3/3 [==============================] - 0s 99ms/step - loss: 0.3905 - accuracy: 0.8447 - val_loss: 0.4756 - val_accuracy: 0.8000
Epoch 101/150
3/3 [==============================] - 0s 86ms/step - loss: 0.4529 - accuracy: 0.8026 - val_loss: 0.4203 - val_accuracy: 0.8842
Epoch 46/150
3/3 [==============================] - 0s 95ms/step - loss: 0.3721 - accuracy: 0.8526 - val_loss: 0.4758 - val_accuracy: 0.8000
Epoch 102/150
3/3 [==============================] - 0s 64ms/step - loss: 0.4040 - accuracy: 0.8289 - val_loss: 0.4171 - val_accuracy: 0.8842
Epoch 47/150
3/3 [==============================] - 0s 87ms/step - loss: 0.3871 - accuracy: 0.8500 - val_loss: 0.4756 - val_accuracy: 0.8000
Epoch 103/150
3/3 [==============================] - 0s 77ms/step - loss: 0.4026 - accuracy: 0.8158 - val_loss: 0.4144 - val_accuracy: 0.8842
Epoch 48/150
3/3 [==============================] - 0s 84ms/step - loss: 0.3735 - accuracy: 0.8526 - val_loss: 0.4754 - val_accuracy: 0.8105
Epoch 104/150
3/3 [==============================] - 0s 64ms/step - loss: 0.4389 - accuracy: 0.8053 - val_loss: 0.4118 - val_accuracy: 0.8842
Epoch 49/150
3/3 [==============================] - 0s 82ms/step - loss: 0.3900 - accuracy: 0.8184 - val_loss: 0.4752 - val_accuracy: 0.8000
Epoch 105/150
3/3 [==============================] - 0s 72ms/step - loss: 0.4570 - accuracy: 0.8026 - val_loss: 0.4093 - val_accuracy: 0.8842
Epoch 50/150
3/3 [==============================] - 0s 76ms/step - loss: 0.4455 - accuracy: 0.8132 - val_loss: 0.4078 - val_accuracy: 0.8842
3/3 [==============================] - 0s 79ms/step - loss: 0.3972 - accuracy: 0.8368 - val_loss: 0.4750 - val_accuracy: 0.8000
Epoch 51/150
Epoch 106/150
3/3 [==============================] - 0s 75ms/step - loss: 0.4049 - accuracy: 0.8342 - val_loss: 0.4752 - val_accuracy: 0.8000
3/3 [==============================] - 0s 75ms/step - loss: 0.4596 - accuracy: 0.7868 - val_loss: 0.4066 - val_accuracy: 0.8842
Epoch 52/150
Epoch 107/150
3/3 [==============================] - 0s 73ms/step - loss: 0.3755 - accuracy: 0.8605 - val_loss: 0.4756 - val_accuracy: 0.8000
3/3 [==============================] - 0s 78ms/step - loss: 0.4404 - accuracy: 0.8184 - val_loss: 0.4054 - val_accuracy: 0.8842
Epoch 108/150
Epoch 53/150
3/3 [==============================] - 0s 82ms/step - loss: 0.4525 - accuracy: 0.8026 - val_loss: 0.4041 - val_accuracy: 0.8842
Epoch 54/150
3/3 [==============================] - 0s 99ms/step - loss: 0.3575 - accuracy: 0.8474 - val_loss: 0.4760 - val_accuracy: 0.8105
1/3 [=========>....................] - ETA: 0s - loss: 0.5273 - accuracy: 0.7734Epoch 109/150
3/3 [==============================] - 0s 80ms/step - loss: 0.4665 - accuracy: 0.8026 - val_loss: 0.4030 - val_accuracy: 0.8842
Epoch 55/150
3/3 [==============================] - 0s 81ms/step - loss: 0.3844 - accuracy: 0.8526 - val_loss: 0.4766 - val_accuracy: 0.8105
Epoch 110/150
3/3 [==============================] - 0s 83ms/step - loss: 0.4582 - accuracy: 0.7895 - val_loss: 0.4018 - val_accuracy: 0.8842
Epoch 56/150
3/3 [==============================] - 0s 86ms/step - loss: 0.3726 - accuracy: 0.8526 - val_loss: 0.4771 - val_accuracy: 0.8105
Epoch 111/150
3/3 [==============================] - 0s 71ms/step - loss: 0.3889 - accuracy: 0.8316 - val_loss: 0.4775 - val_accuracy: 0.8105
Epoch 112/150
3/3 [==============================] - 0s 102ms/step - loss: 0.4038 - accuracy: 0.8211 - val_loss: 0.4005 - val_accuracy: 0.8842
1/3 [=========>....................] - ETA: 0s - loss: 0.4038 - accuracy: 0.8125Epoch 57/150
3/3 [==============================] - 0s 66ms/step - loss: 0.4592 - accuracy: 0.7816 - val_loss: 0.3994 - val_accuracy: 0.8842
3/3 [==============================] - 0s 82ms/step - loss: 0.3912 - accuracy: 0.8263 - val_loss: 0.4778 - val_accuracy: 0.8000
Epoch 113/150
Epoch 58/150
3/3 [==============================] - 0s 68ms/step - loss: 0.4483 - accuracy: 0.7974 - val_loss: 0.3983 - val_accuracy: 0.8842
Epoch 59/150
3/3 [==============================] - 0s 84ms/step - loss: 0.3940 - accuracy: 0.8421 - val_loss: 0.4780 - val_accuracy: 0.8000
Epoch 114/150
3/3 [==============================] - 0s 71ms/step - loss: 0.4522 - accuracy: 0.8079 - val_loss: 0.3969 - val_accuracy: 0.8842
Epoch 60/150
3/3 [==============================] - 0s 78ms/step - loss: 0.3693 - accuracy: 0.8553 - val_loss: 0.4789 - val_accuracy: 0.8000
Epoch 115/150
3/3 [==============================] - 0s 71ms/step - loss: 0.4504 - accuracy: 0.8053 - val_loss: 0.3961 - val_accuracy: 0.8842
Epoch 61/150
3/3 [==============================] - 0s 74ms/step - loss: 0.3847 - accuracy: 0.8474 - val_loss: 0.4797 - val_accuracy: 0.8000
Epoch 116/150
3/3 [==============================] - 0s 71ms/step - loss: 0.3991 - accuracy: 0.8342 - val_loss: 0.3948 - val_accuracy: 0.8842
Epoch 62/150
3/3 [==============================] - 0s 69ms/step - loss: 0.3723 - accuracy: 0.8474 - val_loss: 0.4803 - val_accuracy: 0.8105
Epoch 117/150
3/3 [==============================] - 0s 70ms/step - loss: 0.3919 - accuracy: 0.8500 - val_loss: 0.4810 - val_accuracy: 0.8105
3/3 [==============================] - 0s 72ms/step - loss: 0.4357 - accuracy: 0.7947 - val_loss: 0.3931 - val_accuracy: 0.8842
Epoch 63/150
Epoch 118/150
3/3 [==============================] - 0s 69ms/step - loss: 0.4537 - accuracy: 0.8079 - val_loss: 0.3919 - val_accuracy: 0.8842
3/3 [==============================] - 0s 75ms/step - loss: 0.3818 - accuracy: 0.8368 - val_loss: 0.4818 - val_accuracy: 0.8105
Epoch 119/150
Epoch 64/150
3/3 [==============================] - 0s 72ms/step - loss: 0.4098 - accuracy: 0.8289 - val_loss: 0.3909 - val_accuracy: 0.8842
Epoch 65/150
3/3 [==============================] - 0s 80ms/step - loss: 0.3863 - accuracy: 0.8447 - val_loss: 0.4823 - val_accuracy: 0.8105
Epoch 120/150
3/3 [==============================] - 0s 68ms/step - loss: 0.3719 - accuracy: 0.8526 - val_loss: 0.4828 - val_accuracy: 0.8105
Epoch 121/150
3/3 [==============================] - 0s 74ms/step - loss: 0.4325 - accuracy: 0.7947 - val_loss: 0.3900 - val_accuracy: 0.8842
Epoch 66/150
3/3 [==============================] - 0s 76ms/step - loss: 0.4108 - accuracy: 0.8263 - val_loss: 0.4832 - val_accuracy: 0.8105
Epoch 122/150
3/3 [==============================] - 0s 77ms/step - loss: 0.4182 - accuracy: 0.8342 - val_loss: 0.3889 - val_accuracy: 0.8842
Epoch 67/150
3/3 [==============================] - 0s 63ms/step - loss: 0.4345 - accuracy: 0.8158 - val_loss: 0.3882 - val_accuracy: 0.8842
Epoch 68/150
3/3 [==============================] - 0s 67ms/step - loss: 0.3922 - accuracy: 0.8553 - val_loss: 0.4835 - val_accuracy: 0.8105
Epoch 123/150
3/3 [==============================] - 0s 79ms/step - loss: 0.4485 - accuracy: 0.8105 - val_loss: 0.3875 - val_accuracy: 0.8842
3/3 [==============================] - 0s 82ms/step - loss: 0.3681 - accuracy: 0.8526 - val_loss: 0.4844 - val_accuracy: 0.8105
Epoch 69/150
Epoch 124/150
3/3 [==============================] - 0s 67ms/step - loss: 0.3926 - accuracy: 0.8447 - val_loss: 0.4853 - val_accuracy: 0.8105
Epoch 125/150
3/3 [==============================] - 0s 73ms/step - loss: 0.4649 - accuracy: 0.8000 - val_loss: 0.3864 - val_accuracy: 0.8842
Epoch 70/150
3/3 [==============================] - 0s 79ms/step - loss: 0.3844 - accuracy: 0.8342 - val_loss: 0.4862 - val_accuracy: 0.8105
Epoch 126/150
3/3 [==============================] - 0s 79ms/step - loss: 0.4399 - accuracy: 0.8132 - val_loss: 0.3857 - val_accuracy: 0.8842
Epoch 71/150
3/3 [==============================] - 0s 56ms/step - loss: 0.3918 - accuracy: 0.8289 - val_loss: 0.3847 - val_accuracy: 0.8842
3/3 [==============================] - 0s 65ms/step - loss: 0.3947 - accuracy: 0.8289 - val_loss: 0.4866 - val_accuracy: 0.8105
Epoch 127/150
Epoch 72/150
3/3 [==============================] - 0s 68ms/step - loss: 0.3764 - accuracy: 0.8474 - val_loss: 0.4868 - val_accuracy: 0.8105
Epoch 128/150
3/3 [==============================] - 0s 73ms/step - loss: 0.4218 - accuracy: 0.8263 - val_loss: 0.3841 - val_accuracy: 0.8842
Epoch 73/150
3/3 [==============================] - 0s 64ms/step - loss: 0.3881 - accuracy: 0.8474 - val_loss: 0.4867 - val_accuracy: 0.8105
Epoch 129/150
3/3 [==============================] - 0s 120ms/step - loss: 0.4522 - accuracy: 0.8105 - val_loss: 0.3837 - val_accuracy: 0.8842
Epoch 74/150
3/3 [==============================] - 0s 115ms/step - loss: 0.3632 - accuracy: 0.8579 - val_loss: 0.4865 - val_accuracy: 0.8105
Epoch 130/150
3/3 [==============================] - 0s 110ms/step - loss: 0.4217 - accuracy: 0.8105 - val_loss: 0.3828 - val_accuracy: 0.8842
Epoch 75/150
3/3 [==============================] - 0s 85ms/step - loss: 0.3946 - accuracy: 0.8342 - val_loss: 0.4863 - val_accuracy: 0.8105
Epoch 131/150
3/3 [==============================] - 0s 81ms/step - loss: 0.4090 - accuracy: 0.8263 - val_loss: 0.3823 - val_accuracy: 0.8842
Epoch 76/150
3/3 [==============================] - 0s 79ms/step - loss: 0.4029 - accuracy: 0.8316 - val_loss: 0.4859 - val_accuracy: 0.8105
Epoch 132/150
3/3 [==============================] - 0s 72ms/step - loss: 0.3908 - accuracy: 0.8237 - val_loss: 0.3811 - val_accuracy: 0.8842
Epoch 77/150
3/3 [==============================] - 0s 66ms/step - loss: 0.3881 - accuracy: 0.8368 - val_loss: 0.4852 - val_accuracy: 0.8105
Epoch 133/150
3/3 [==============================] - 0s 63ms/step - loss: 0.4244 - accuracy: 0.7974 - val_loss: 0.3800 - val_accuracy: 0.8842
Epoch 78/150
3/3 [==============================] - 0s 77ms/step - loss: 0.3719 - accuracy: 0.8500 - val_loss: 0.4850 - val_accuracy: 0.8105
Epoch 134/150
3/3 [==============================] - 0s 88ms/step - loss: 0.4470 - accuracy: 0.7921 - val_loss: 0.3786 - val_accuracy: 0.8842
Epoch 79/150
3/3 [==============================] - 0s 94ms/step - loss: 0.3888 - accuracy: 0.8395 - val_loss: 0.4851 - val_accuracy: 0.8105
Epoch 135/150
3/3 [==============================] - 0s 98ms/step - loss: 0.4230 - accuracy: 0.8158 - val_loss: 0.3774 - val_accuracy: 0.8842
Epoch 80/150
3/3 [==============================] - 0s 90ms/step - loss: 0.3837 - accuracy: 0.8474 - val_loss: 0.4854 - val_accuracy: 0.8105
Epoch 136/150
3/3 [==============================] - 0s 134ms/step - loss: 0.3859 - accuracy: 0.8105 - val_loss: 0.3769 - val_accuracy: 0.8842
Epoch 81/150
3/3 [==============================] - 0s 96ms/step - loss: 0.3770 - accuracy: 0.8421 - val_loss: 0.4857 - val_accuracy: 0.8105
Epoch 137/150
3/3 [==============================] - 0s 91ms/step - loss: 0.4218 - accuracy: 0.8211 - val_loss: 0.3765 - val_accuracy: 0.8842
Epoch 82/150
3/3 [==============================] - 0s 96ms/step - loss: 0.3831 - accuracy: 0.8368 - val_loss: 0.4857 - val_accuracy: 0.8105
Epoch 138/150
3/3 [==============================] - 0s 96ms/step - loss: 0.4145 - accuracy: 0.8158 - val_loss: 0.3758 - val_accuracy: 0.8842
Epoch 83/150
3/3 [==============================] - 0s 86ms/step - loss: 0.3569 - accuracy: 0.8579 - val_loss: 0.4860 - val_accuracy: 0.8105
Epoch 139/150
3/3 [==============================] - 0s 104ms/step - loss: 0.4124 - accuracy: 0.8184 - val_loss: 0.3749 - val_accuracy: 0.8842
Epoch 84/150
3/3 [==============================] - 0s 97ms/step - loss: 0.3882 - accuracy: 0.8421 - val_loss: 0.4863 - val_accuracy: 0.8105
Epoch 140/150
3/3 [==============================] - 0s 105ms/step - loss: 0.4035 - accuracy: 0.8158 - val_loss: 0.3734 - val_accuracy: 0.8842
Epoch 85/150
3/3 [==============================] - 0s 93ms/step - loss: 0.3700 - accuracy: 0.8368 - val_loss: 0.4867 - val_accuracy: 0.8105
Epoch 141/150
3/3 [==============================] - 0s 74ms/step - loss: 0.3941 - accuracy: 0.8342 - val_loss: 0.4874 - val_accuracy: 0.8105
Epoch 142/150
3/3 [==============================] - 0s 119ms/step - loss: 0.4403 - accuracy: 0.8211 - val_loss: 0.3728 - val_accuracy: 0.8842
Epoch 86/150
3/3 [==============================] - 0s 75ms/step - loss: 0.3665 - accuracy: 0.8395 - val_loss: 0.4884 - val_accuracy: 0.8105
Epoch 143/150
3/3 [==============================] - 0s 53ms/step - loss: 0.3643 - accuracy: 0.8605 - val_loss: 0.4895 - val_accuracy: 0.8000
Epoch 144/150
3/3 [==============================] - 0s 123ms/step - loss: 0.4576 - accuracy: 0.8158 - val_loss: 0.3716 - val_accuracy: 0.8842
Epoch 87/150
3/3 [==============================] - 0s 78ms/step - loss: 0.3815 - accuracy: 0.8500 - val_loss: 0.4905 - val_accuracy: 0.8000
Epoch 145/150
3/3 [==============================] - 0s 81ms/step - loss: 0.4105 - accuracy: 0.8132 - val_loss: 0.3709 - val_accuracy: 0.8842
Epoch 88/150
3/3 [==============================] - 0s 71ms/step - loss: 0.3495 - accuracy: 0.8474 - val_loss: 0.4911 - val_accuracy: 0.8000
Epoch 146/150
3/3 [==============================] - 0s 84ms/step - loss: 0.4277 - accuracy: 0.8079 - val_loss: 0.3703 - val_accuracy: 0.8842
Epoch 89/150
3/3 [==============================] - 0s 88ms/step - loss: 0.3803 - accuracy: 0.8447 - val_loss: 0.4917 - val_accuracy: 0.8000
Epoch 147/150
3/3 [==============================] - 0s 90ms/step - loss: 0.4177 - accuracy: 0.8132 - val_loss: 0.3696 - val_accuracy: 0.8842
Epoch 90/150
3/3 [==============================] - 0s 98ms/step - loss: 0.3504 - accuracy: 0.8474 - val_loss: 0.4922 - val_accuracy: 0.8000
Epoch 148/150
3/3 [==============================] - 0s 91ms/step - loss: 0.4204 - accuracy: 0.8289 - val_loss: 0.3694 - val_accuracy: 0.8842
Epoch 91/150
3/3 [==============================] - 0s 77ms/step - loss: 0.3611 - accuracy: 0.8553 - val_loss: 0.4929 - val_accuracy: 0.8000
Epoch 149/150
3/3 [==============================] - 0s 89ms/step - loss: 0.4462 - accuracy: 0.7921 - val_loss: 0.3690 - val_accuracy: 0.8842
Epoch 92/150
3/3 [==============================] - 0s 89ms/step - loss: 0.3612 - accuracy: 0.8658 - val_loss: 0.4934 - val_accuracy: 0.8000
Epoch 150/150
3/3 [==============================] - 0s 86ms/step - loss: 0.4243 - accuracy: 0.8105 - val_loss: 0.3691 - val_accuracy: 0.8842
Epoch 93/150
3/3 [==============================] - 0s 108ms/step - loss: 0.3747 - accuracy: 0.8474 - val_loss: 0.4940 - val_accuracy: 0.8000
3/3 [==============================] - 0s 102ms/step - loss: 0.4154 - accuracy: 0.8368 - val_loss: 0.3686 - val_accuracy: 0.8842
Epoch 94/150
3/3 [==============================] - 0s 98ms/step - loss: 0.4276 - accuracy: 0.8105 - val_loss: 0.3682 - val_accuracy: 0.8842
Epoch 95/150
3/3 [==============================] - 0s 77ms/step - loss: 0.3868 - accuracy: 0.8237 - val_loss: 0.3682 - val_accuracy: 0.8842
1/2 [==============>...............] - ETA: 0s - loss: 0.4847 - accuracy: 0.8281Epoch 96/150
2/2 [==============================] - 0s 41ms/step - loss: 0.4141 - accuracy: 0.8523
3/3 [==============================] - 0s 84ms/step - loss: 0.3970 - accuracy: 0.8368 - val_loss: 0.3674 - val_accuracy: 0.8842
Epoch 97/150
3/3 [==============================] - 0s 75ms/step - loss: 0.4164 - accuracy: 0.8158 - val_loss: 0.3670 - val_accuracy: 0.8842
Epoch 98/150
3/3 [==============================] - 0s 73ms/step - loss: 0.4562 - accuracy: 0.7895 - val_loss: 0.3667 - val_accuracy: 0.8842
Epoch 99/150
3/3 [==============================] - 0s 71ms/step - loss: 0.4149 - accuracy: 0.8132 - val_loss: 0.3659 - val_accuracy: 0.8842
Epoch 100/150
1/3 [=========>....................] - ETA: 0s - loss: 0.3597 - accuracy: 0.8281Epoch 1/150
3/3 [==============================] - 0s 79ms/step - loss: 0.4026 - accuracy: 0.8263 - val_loss: 0.3657 - val_accuracy: 0.8842
Epoch 101/150
3/3 [==============================] - 0s 67ms/step - loss: 0.3926 - accuracy: 0.8211 - val_loss: 0.3655 - val_accuracy: 0.8842
Epoch 102/150
3/3 [==============================] - 0s 68ms/step - loss: 0.4411 - accuracy: 0.8000 - val_loss: 0.3651 - val_accuracy: 0.8842
Epoch 103/150
3/3 [==============================] - 0s 90ms/step - loss: 0.4073 - accuracy: 0.8132 - val_loss: 0.3649 - val_accuracy: 0.8842
Epoch 104/150
3/3 [==============================] - 0s 71ms/step - loss: 0.4010 - accuracy: 0.8316 - val_loss: 0.3654 - val_accuracy: 0.8842
Epoch 105/150
3/3 [==============================] - 0s 77ms/step - loss: 0.4187 - accuracy: 0.8105 - val_loss: 0.3657 - val_accuracy: 0.8842
Epoch 106/150
3/3 [==============================] - 0s 66ms/step - loss: 0.4291 - accuracy: 0.8026 - val_loss: 0.3654 - val_accuracy: 0.8842
Epoch 107/150
3/3 [==============================] - 0s 54ms/step - loss: 0.4006 - accuracy: 0.8237 - val_loss: 0.3643 - val_accuracy: 0.8842
Epoch 108/150
3/3 [==============================] - 0s 79ms/step - loss: 0.4134 - accuracy: 0.8158 - val_loss: 0.3639 - val_accuracy: 0.8842
Epoch 109/150
3/3 [==============================] - 0s 85ms/step - loss: 0.3963 - accuracy: 0.8447 - val_loss: 0.3636 - val_accuracy: 0.8947
Epoch 110/150
3/3 [==============================] - 0s 67ms/step - loss: 0.4303 - accuracy: 0.8053 - val_loss: 0.3635 - val_accuracy: 0.8947
Epoch 111/150
3/3 [==============================] - 0s 84ms/step - loss: 0.4070 - accuracy: 0.8289 - val_loss: 0.3636 - val_accuracy: 0.8947
Epoch 112/150
3/3 [==============================] - 0s 71ms/step - loss: 0.4361 - accuracy: 0.8184 - val_loss: 0.3639 - val_accuracy: 0.8842
Epoch 113/150
3/3 [==============================] - 0s 78ms/step - loss: 0.4079 - accuracy: 0.8132 - val_loss: 0.3640 - val_accuracy: 0.8947
Epoch 114/150
3/3 [==============================] - 0s 82ms/step - loss: 0.4212 - accuracy: 0.8184 - val_loss: 0.3629 - val_accuracy: 0.8947
Epoch 115/150
3/3 [==============================] - 0s 70ms/step - loss: 0.3948 - accuracy: 0.8342 - val_loss: 0.3616 - val_accuracy: 0.8842
Epoch 116/150
3/3 [==============================] - 0s 56ms/step - loss: 0.4152 - accuracy: 0.8105 - val_loss: 0.3604 - val_accuracy: 0.8842
Epoch 117/150
3/3 [==============================] - 0s 59ms/step - loss: 0.4230 - accuracy: 0.8184 - val_loss: 0.3597 - val_accuracy: 0.8737
Epoch 118/150
3/3 [==============================] - 0s 61ms/step - loss: 0.4114 - accuracy: 0.8237 - val_loss: 0.3593 - val_accuracy: 0.8737
Epoch 119/150
3/3 [==============================] - 0s 71ms/step - loss: 0.3914 - accuracy: 0.8079 - val_loss: 0.3591 - val_accuracy: 0.8737
Epoch 120/150
3/3 [==============================] - 0s 57ms/step - loss: 0.3944 - accuracy: 0.8184 - val_loss: 0.3595 - val_accuracy: 0.8737
Epoch 121/150
3/3 [==============================] - 0s 62ms/step - loss: 0.4006 - accuracy: 0.8316 - val_loss: 0.3594 - val_accuracy: 0.8737
Epoch 122/150
3/3 [==============================] - 0s 65ms/step - loss: 0.4027 - accuracy: 0.8368 - val_loss: 0.3594 - val_accuracy: 0.8737
Epoch 123/150
3/3 [==============================] - 0s 64ms/step - loss: 0.4084 - accuracy: 0.8289 - val_loss: 0.3593 - val_accuracy: 0.8737
Epoch 124/150
3/3 [==============================] - 0s 64ms/step - loss: 0.4341 - accuracy: 0.8079 - val_loss: 0.3597 - val_accuracy: 0.8737
Epoch 125/150
3/3 [==============================] - 0s 72ms/step - loss: 0.4015 - accuracy: 0.8026 - val_loss: 0.3600 - val_accuracy: 0.8737
Epoch 126/150
3/3 [==============================] - 0s 78ms/step - loss: 0.4139 - accuracy: 0.8158 - val_loss: 0.3597 - val_accuracy: 0.8737
Epoch 127/150
3/3 [==============================] - 0s 53ms/step - loss: 0.3842 - accuracy: 0.8421 - val_loss: 0.3597 - val_accuracy: 0.8737
Epoch 128/150
3/3 [==============================] - 0s 78ms/step - loss: 0.4148 - accuracy: 0.8026 - val_loss: 0.3594 - val_accuracy: 0.8842
Epoch 129/150
3/3 [==============================] - 0s 71ms/step - loss: 0.3993 - accuracy: 0.8316 - val_loss: 0.3596 - val_accuracy: 0.8842
Epoch 130/150
3/3 [==============================] - 0s 66ms/step - loss: 0.4012 - accuracy: 0.8368 - val_loss: 0.3595 - val_accuracy: 0.8842
Epoch 131/150
3/3 [==============================] - 0s 68ms/step - loss: 0.4114 - accuracy: 0.8263 - val_loss: 0.3593 - val_accuracy: 0.8842
Epoch 132/150
3/3 [==============================] - 0s 69ms/step - loss: 0.4413 - accuracy: 0.7842 - val_loss: 0.3587 - val_accuracy: 0.8737
Epoch 133/150
3/3 [==============================] - 0s 71ms/step - loss: 0.3901 - accuracy: 0.8263 - val_loss: 0.3581 - val_accuracy: 0.8737
Epoch 134/150
3/3 [==============================] - 6s 574ms/step - loss: 0.8278 - accuracy: 0.5435 - val_loss: 0.7094 - val_accuracy: 0.4737
Epoch 2/150
3/3 [==============================] - 0s 110ms/step - loss: 0.3948 - accuracy: 0.8184 - val_loss: 0.3580 - val_accuracy: 0.8737
Epoch 135/150
3/3 [==============================] - 0s 96ms/step - loss: 0.7668 - accuracy: 0.5831 - val_loss: 0.6987 - val_accuracy: 0.5053
Epoch 3/150
3/3 [==============================] - 0s 85ms/step - loss: 0.4111 - accuracy: 0.8026 - val_loss: 0.3583 - val_accuracy: 0.8737
Epoch 136/150
3/3 [==============================] - 0s 65ms/step - loss: 0.8295 - accuracy: 0.5699 - val_loss: 0.6889 - val_accuracy: 0.5789
Epoch 4/150
3/3 [==============================] - 0s 68ms/step - loss: 0.3983 - accuracy: 0.8368 - val_loss: 0.3579 - val_accuracy: 0.8737
3/3 [==============================] - 0s 55ms/step - loss: 0.7160 - accuracy: 0.5989 - val_loss: 0.6806 - val_accuracy: 0.6105
Epoch 5/150
Epoch 137/150
3/3 [==============================] - 0s 68ms/step - loss: 0.7216 - accuracy: 0.6069 - val_loss: 0.6724 - val_accuracy: 0.6211
Epoch 6/150
3/3 [==============================] - 0s 65ms/step - loss: 0.3864 - accuracy: 0.8421 - val_loss: 0.3573 - val_accuracy: 0.8737
Epoch 138/150
3/3 [==============================] - 0s 48ms/step - loss: 0.6788 - accuracy: 0.6306 - val_loss: 0.6643 - val_accuracy: 0.6211
Epoch 7/150
3/3 [==============================] - 0s 67ms/step - loss: 0.4174 - accuracy: 0.8211 - val_loss: 0.3574 - val_accuracy: 0.8737
Epoch 139/150
3/3 [==============================] - 0s 78ms/step - loss: 0.7530 - accuracy: 0.5884 - val_loss: 0.6561 - val_accuracy: 0.6632
Epoch 8/150
3/3 [==============================] - 0s 81ms/step - loss: 0.4328 - accuracy: 0.8053 - val_loss: 0.3577 - val_accuracy: 0.8737
Epoch 140/150
3/3 [==============================] - 0s 72ms/step - loss: 0.6679 - accuracy: 0.6596 - val_loss: 0.6484 - val_accuracy: 0.6526
Epoch 9/150
3/3 [==============================] - 0s 65ms/step - loss: 0.3794 - accuracy: 0.8263 - val_loss: 0.3583 - val_accuracy: 0.8737
Epoch 141/150
3/3 [==============================] - 0s 72ms/step - loss: 0.6273 - accuracy: 0.6807 - val_loss: 0.6423 - val_accuracy: 0.6526
Epoch 10/150
3/3 [==============================] - 0s 60ms/step - loss: 0.3980 - accuracy: 0.8289 - val_loss: 0.3585 - val_accuracy: 0.8737
Epoch 142/150
3/3 [==============================] - 0s 61ms/step - loss: 0.6680 - accuracy: 0.6623 - val_loss: 0.6360 - val_accuracy: 0.7158
Epoch 11/150
3/3 [==============================] - 0s 62ms/step - loss: 0.3905 - accuracy: 0.8237 - val_loss: 0.3585 - val_accuracy: 0.8737
Epoch 143/150
3/3 [==============================] - 0s 59ms/step - loss: 0.4119 - accuracy: 0.8184 - val_loss: 0.3585 - val_accuracy: 0.8737
Epoch 144/150
3/3 [==============================] - 0s 70ms/step - loss: 0.6183 - accuracy: 0.6834 - val_loss: 0.6296 - val_accuracy: 0.7158
Epoch 12/150
3/3 [==============================] - 0s 57ms/step - loss: 0.6126 - accuracy: 0.7177 - val_loss: 0.6230 - val_accuracy: 0.7158
Epoch 13/150
3/3 [==============================] - 0s 62ms/step - loss: 0.3514 - accuracy: 0.8553 - val_loss: 0.3583 - val_accuracy: 0.8737
Epoch 145/150
3/3 [==============================] - 0s 60ms/step - loss: 0.6280 - accuracy: 0.6570 - val_loss: 0.6168 - val_accuracy: 0.7263
3/3 [==============================] - 0s 61ms/step - loss: 0.4123 - accuracy: 0.8237 - val_loss: 0.3581 - val_accuracy: 0.8737
Epoch 146/150
Epoch 14/150
3/3 [==============================] - 0s 66ms/step - loss: 0.4024 - accuracy: 0.8395 - val_loss: 0.3584 - val_accuracy: 0.8737
Epoch 147/150
3/3 [==============================] - 0s 71ms/step - loss: 0.6252 - accuracy: 0.6860 - val_loss: 0.6108 - val_accuracy: 0.7158
Epoch 15/150
3/3 [==============================] - 0s 58ms/step - loss: 0.5758 - accuracy: 0.7230 - val_loss: 0.6056 - val_accuracy: 0.7158
Epoch 16/150
3/3 [==============================] - 0s 66ms/step - loss: 0.3891 - accuracy: 0.8342 - val_loss: 0.3589 - val_accuracy: 0.8737
Epoch 148/150
3/3 [==============================] - 0s 59ms/step - loss: 0.4214 - accuracy: 0.7974 - val_loss: 0.3589 - val_accuracy: 0.8737
3/3 [==============================] - 0s 68ms/step - loss: 0.5960 - accuracy: 0.6992 - val_loss: 0.6003 - val_accuracy: 0.7368
Epoch 149/150
Epoch 17/150
3/3 [==============================] - 0s 59ms/step - loss: 0.5537 - accuracy: 0.7150 - val_loss: 0.5957 - val_accuracy: 0.7368
Epoch 18/150
3/3 [==============================] - 0s 68ms/step - loss: 0.3907 - accuracy: 0.8342 - val_loss: 0.3593 - val_accuracy: 0.8737
Epoch 150/150
3/3 [==============================] - 0s 54ms/step - loss: 0.3971 - accuracy: 0.8342 - val_loss: 0.3592 - val_accuracy: 0.8737
3/3 [==============================] - 0s 64ms/step - loss: 0.5863 - accuracy: 0.7309 - val_loss: 0.5908 - val_accuracy: 0.7368
Epoch 19/150
3/3 [==============================] - 0s 39ms/step - loss: 0.5382 - accuracy: 0.7335 - val_loss: 0.5860 - val_accuracy: 0.7368
Epoch 20/150
3/3 [==============================] - 0s 39ms/step - loss: 0.5824 - accuracy: 0.7071 - val_loss: 0.5818 - val_accuracy: 0.7368
Epoch 21/150
3/3 [==============================] - 0s 31ms/step - loss: 0.5540 - accuracy: 0.7388 - val_loss: 0.5773 - val_accuracy: 0.7368
Epoch 22/150
3/3 [==============================] - 0s 38ms/step - loss: 0.5719 - accuracy: 0.6939 - val_loss: 0.5731 - val_accuracy: 0.7368
Epoch 23/150
3/3 [==============================] - 0s 41ms/step - loss: 0.5513 - accuracy: 0.7256 - val_loss: 0.5686 - val_accuracy: 0.7368
Epoch 24/150
3/3 [==============================] - 0s 41ms/step - loss: 0.5455 - accuracy: 0.7203 - val_loss: 0.5644 - val_accuracy: 0.7263
Epoch 25/150
3/3 [==============================] - 0s 60ms/step - loss: 0.5406 - accuracy: 0.7520 - val_loss: 0.5606 - val_accuracy: 0.7368
Epoch 26/150
3/3 [==============================] - 0s 49ms/step - loss: 0.5795 - accuracy: 0.7045 - val_loss: 0.5570 - val_accuracy: 0.7368
Epoch 27/150
3/3 [==============================] - 0s 39ms/step - loss: 0.5198 - accuracy: 0.7652 - val_loss: 0.5532 - val_accuracy: 0.7263
Epoch 28/150
3/3 [==============================] - 0s 49ms/step - loss: 0.5009 - accuracy: 0.7520 - val_loss: 0.5495 - val_accuracy: 0.7263
Epoch 29/150
3/3 [==============================] - 0s 39ms/step - loss: 0.5226 - accuracy: 0.7573 - val_loss: 0.5460 - val_accuracy: 0.7263
Epoch 30/150
3/3 [==============================] - 0s 42ms/step - loss: 0.5147 - accuracy: 0.7520 - val_loss: 0.5428 - val_accuracy: 0.7263
Epoch 31/150
3/3 [==============================] - 0s 39ms/step - loss: 0.5314 - accuracy: 0.7625 - val_loss: 0.5399 - val_accuracy: 0.7368
Epoch 32/150
3/3 [==============================] - 0s 39ms/step - loss: 0.5273 - accuracy: 0.7652 - val_loss: 0.5370 - val_accuracy: 0.7368
Epoch 33/150
3/3 [==============================] - 0s 47ms/step - loss: 0.5277 - accuracy: 0.7625 - val_loss: 0.5343 - val_accuracy: 0.7368
Epoch 34/150
3/3 [==============================] - 0s 30ms/step - loss: 0.4856 - accuracy: 0.7599 - val_loss: 0.5309 - val_accuracy: 0.7368
Epoch 35/150
3/3 [==============================] - 0s 39ms/step - loss: 0.4787 - accuracy: 0.7836 - val_loss: 0.5279 - val_accuracy: 0.7368
Epoch 36/150
3/3 [==============================] - 0s 38ms/step - loss: 0.4612 - accuracy: 0.8127 - val_loss: 0.5251 - val_accuracy: 0.7263
Epoch 37/150
3/3 [==============================] - 0s 42ms/step - loss: 0.5410 - accuracy: 0.7335 - val_loss: 0.5223 - val_accuracy: 0.7474
Epoch 38/150
3/3 [==============================] - 0s 39ms/step - loss: 0.4965 - accuracy: 0.7414 - val_loss: 0.5195 - val_accuracy: 0.7474
Epoch 39/150
3/3 [==============================] - 0s 38ms/step - loss: 0.4910 - accuracy: 0.7625 - val_loss: 0.5167 - val_accuracy: 0.7474
Epoch 40/150
3/3 [==============================] - 0s 30ms/step - loss: 0.5221 - accuracy: 0.7546 - val_loss: 0.5141 - val_accuracy: 0.7474
Epoch 41/150
3/3 [==============================] - 0s 40ms/step - loss: 0.4933 - accuracy: 0.7625 - val_loss: 0.5117 - val_accuracy: 0.7368
Epoch 42/150
3/3 [==============================] - 0s 33ms/step - loss: 0.4664 - accuracy: 0.7863 - val_loss: 0.5094 - val_accuracy: 0.7474
Epoch 43/150
3/3 [==============================] - 0s 31ms/step - loss: 0.4723 - accuracy: 0.7704 - val_loss: 0.5069 - val_accuracy: 0.7474
Epoch 44/150
3/3 [==============================] - 0s 40ms/step - loss: 0.5052 - accuracy: 0.7388 - val_loss: 0.5045 - val_accuracy: 0.7474
Epoch 45/150
3/3 [==============================] - 0s 34ms/step - loss: 0.5207 - accuracy: 0.7678 - val_loss: 0.5026 - val_accuracy: 0.7474
Epoch 46/150
3/3 [==============================] - 0s 49ms/step - loss: 0.5111 - accuracy: 0.7784 - val_loss: 0.5007 - val_accuracy: 0.7368
Epoch 47/150
3/3 [==============================] - 0s 53ms/step - loss: 0.4565 - accuracy: 0.8021 - val_loss: 0.4989 - val_accuracy: 0.7368
Epoch 48/150
3/3 [==============================] - 0s 51ms/step - loss: 0.4876 - accuracy: 0.7625 - val_loss: 0.4971 - val_accuracy: 0.7474
Epoch 49/150
3/3 [==============================] - 0s 47ms/step - loss: 0.4657 - accuracy: 0.7836 - val_loss: 0.4954 - val_accuracy: 0.7474
Epoch 50/150
3/3 [==============================] - 0s 54ms/step - loss: 0.4617 - accuracy: 0.7810 - val_loss: 0.4935 - val_accuracy: 0.7579
Epoch 51/150
3/3 [==============================] - 0s 53ms/step - loss: 0.4723 - accuracy: 0.7704 - val_loss: 0.4917 - val_accuracy: 0.7579
Epoch 52/150
3/3 [==============================] - 0s 53ms/step - loss: 0.4596 - accuracy: 0.7863 - val_loss: 0.4901 - val_accuracy: 0.7579
Epoch 53/150
3/3 [==============================] - 0s 61ms/step - loss: 0.4725 - accuracy: 0.7995 - val_loss: 0.4886 - val_accuracy: 0.7579
Epoch 54/150
3/3 [==============================] - 0s 60ms/step - loss: 0.4904 - accuracy: 0.7784 - val_loss: 0.4871 - val_accuracy: 0.7579
Epoch 55/150
3/3 [==============================] - 0s 58ms/step - loss: 0.4645 - accuracy: 0.7836 - val_loss: 0.4856 - val_accuracy: 0.7579
Epoch 56/150
3/3 [==============================] - 0s 54ms/step - loss: 0.4462 - accuracy: 0.8021 - val_loss: 0.4842 - val_accuracy: 0.7579
Epoch 57/150
3/3 [==============================] - 0s 58ms/step - loss: 0.4683 - accuracy: 0.7810 - val_loss: 0.4828 - val_accuracy: 0.7579
Epoch 58/150
3/3 [==============================] - 0s 57ms/step - loss: 0.4901 - accuracy: 0.7995 - val_loss: 0.4816 - val_accuracy: 0.7579
Epoch 59/150
3/3 [==============================] - 0s 54ms/step - loss: 0.4710 - accuracy: 0.7916 - val_loss: 0.4802 - val_accuracy: 0.7684
Epoch 60/150
3/3 [==============================] - 0s 64ms/step - loss: 0.4758 - accuracy: 0.7757 - val_loss: 0.4793 - val_accuracy: 0.7684
Epoch 61/150
3/3 [==============================] - 0s 55ms/step - loss: 0.4617 - accuracy: 0.7836 - val_loss: 0.4782 - val_accuracy: 0.7684
Epoch 62/150
3/3 [==============================] - 0s 56ms/step - loss: 0.4458 - accuracy: 0.8127 - val_loss: 0.4774 - val_accuracy: 0.7684
Epoch 63/150
3/3 [==============================] - 0s 50ms/step - loss: 0.4365 - accuracy: 0.7942 - val_loss: 0.4764 - val_accuracy: 0.7684
Epoch 64/150
3/3 [==============================] - 0s 50ms/step - loss: 0.4171 - accuracy: 0.8074 - val_loss: 0.4755 - val_accuracy: 0.7684
Epoch 65/150
3/3 [==============================] - 0s 44ms/step - loss: 0.4594 - accuracy: 0.7836 - val_loss: 0.4746 - val_accuracy: 0.7684
Epoch 66/150
3/3 [==============================] - 0s 47ms/step - loss: 0.4374 - accuracy: 0.7942 - val_loss: 0.4737 - val_accuracy: 0.7579
Epoch 67/150
3/3 [==============================] - 0s 62ms/step - loss: 0.4545 - accuracy: 0.7995 - val_loss: 0.4726 - val_accuracy: 0.7579
Epoch 68/150
3/3 [==============================] - 0s 58ms/step - loss: 0.4311 - accuracy: 0.8232 - val_loss: 0.4719 - val_accuracy: 0.7684
Epoch 69/150
3/3 [==============================] - 0s 71ms/step - loss: 0.4688 - accuracy: 0.7863 - val_loss: 0.4714 - val_accuracy: 0.7684
Epoch 70/150
3/3 [==============================] - 0s 44ms/step - loss: 0.4509 - accuracy: 0.7968 - val_loss: 0.4709 - val_accuracy: 0.7684
Epoch 71/150
3/3 [==============================] - 0s 56ms/step - loss: 0.4359 - accuracy: 0.8311 - val_loss: 0.4700 - val_accuracy: 0.7684
Epoch 72/150
3/3 [==============================] - 0s 54ms/step - loss: 0.4539 - accuracy: 0.7942 - val_loss: 0.4696 - val_accuracy: 0.7684
Epoch 73/150
3/3 [==============================] - 0s 45ms/step - loss: 0.4161 - accuracy: 0.8285 - val_loss: 0.4691 - val_accuracy: 0.7684
Epoch 74/150
3/3 [==============================] - 0s 56ms/step - loss: 0.4589 - accuracy: 0.7863 - val_loss: 0.4684 - val_accuracy: 0.7684
Epoch 75/150
3/3 [==============================] - 0s 51ms/step - loss: 0.4519 - accuracy: 0.8338 - val_loss: 0.4681 - val_accuracy: 0.7684
Epoch 76/150
3/3 [==============================] - 0s 46ms/step - loss: 0.4752 - accuracy: 0.7863 - val_loss: 0.4680 - val_accuracy: 0.7684
Epoch 77/150
3/3 [==============================] - 0s 71ms/step - loss: 0.4369 - accuracy: 0.8179 - val_loss: 0.4676 - val_accuracy: 0.7684
Epoch 78/150
3/3 [==============================] - 0s 72ms/step - loss: 0.4612 - accuracy: 0.7916 - val_loss: 0.4674 - val_accuracy: 0.7684
Epoch 79/150
3/3 [==============================] - 0s 56ms/step - loss: 0.4444 - accuracy: 0.8127 - val_loss: 0.4672 - val_accuracy: 0.7684
Epoch 80/150
3/3 [==============================] - 0s 55ms/step - loss: 0.4533 - accuracy: 0.7836 - val_loss: 0.4670 - val_accuracy: 0.7684
Epoch 81/150
3/3 [==============================] - 0s 51ms/step - loss: 0.4536 - accuracy: 0.7995 - val_loss: 0.4665 - val_accuracy: 0.7789
Epoch 82/150
3/3 [==============================] - 0s 58ms/step - loss: 0.4219 - accuracy: 0.8074 - val_loss: 0.4660 - val_accuracy: 0.7789
Epoch 83/150
3/3 [==============================] - 0s 60ms/step - loss: 0.4441 - accuracy: 0.8153 - val_loss: 0.4658 - val_accuracy: 0.7684
Epoch 84/150
3/3 [==============================] - 0s 58ms/step - loss: 0.4110 - accuracy: 0.8232 - val_loss: 0.4657 - val_accuracy: 0.7684
Epoch 85/150
3/3 [==============================] - 0s 49ms/step - loss: 0.4518 - accuracy: 0.8127 - val_loss: 0.4658 - val_accuracy: 0.7684
Epoch 86/150
3/3 [==============================] - 0s 58ms/step - loss: 0.4061 - accuracy: 0.8285 - val_loss: 0.4657 - val_accuracy: 0.7684
Epoch 87/150
3/3 [==============================] - 0s 57ms/step - loss: 0.3900 - accuracy: 0.8153 - val_loss: 0.4654 - val_accuracy: 0.7684
Epoch 88/150
3/3 [==============================] - 0s 56ms/step - loss: 0.4185 - accuracy: 0.8338 - val_loss: 0.4653 - val_accuracy: 0.7684
Epoch 89/150
3/3 [==============================] - 0s 45ms/step - loss: 0.4316 - accuracy: 0.8153 - val_loss: 0.4655 - val_accuracy: 0.7684
Epoch 90/150
3/3 [==============================] - 0s 60ms/step - loss: 0.4287 - accuracy: 0.7810 - val_loss: 0.4656 - val_accuracy: 0.7684
Epoch 91/150
3/3 [==============================] - 0s 50ms/step - loss: 0.4597 - accuracy: 0.8153 - val_loss: 0.4657 - val_accuracy: 0.7684
Epoch 92/150
3/3 [==============================] - 0s 55ms/step - loss: 0.4172 - accuracy: 0.8179 - val_loss: 0.4655 - val_accuracy: 0.7684
Epoch 93/150
3/3 [==============================] - 0s 60ms/step - loss: 0.4230 - accuracy: 0.8100 - val_loss: 0.4657 - val_accuracy: 0.7684
Epoch 94/150
3/3 [==============================] - 0s 57ms/step - loss: 0.4246 - accuracy: 0.7889 - val_loss: 0.4658 - val_accuracy: 0.7684
Epoch 95/150
3/3 [==============================] - 0s 57ms/step - loss: 0.4194 - accuracy: 0.8074 - val_loss: 0.4661 - val_accuracy: 0.7684
Epoch 96/150
3/3 [==============================] - 0s 56ms/step - loss: 0.4202 - accuracy: 0.8153 - val_loss: 0.4664 - val_accuracy: 0.7684
Epoch 97/150
3/3 [==============================] - 0s 52ms/step - loss: 0.4223 - accuracy: 0.8206 - val_loss: 0.4668 - val_accuracy: 0.7684
Epoch 98/150
3/3 [==============================] - 0s 59ms/step - loss: 0.4187 - accuracy: 0.8338 - val_loss: 0.4670 - val_accuracy: 0.7684
Epoch 99/150
3/3 [==============================] - 0s 63ms/step - loss: 0.3939 - accuracy: 0.8470 - val_loss: 0.4669 - val_accuracy: 0.7684
Epoch 100/150
3/3 [==============================] - 0s 60ms/step - loss: 0.3985 - accuracy: 0.8206 - val_loss: 0.4672 - val_accuracy: 0.7684
Epoch 101/150
3/3 [==============================] - 0s 60ms/step - loss: 0.4361 - accuracy: 0.7995 - val_loss: 0.4672 - val_accuracy: 0.7684
Epoch 102/150
3/3 [==============================] - 0s 67ms/step - loss: 0.4510 - accuracy: 0.8021 - val_loss: 0.4676 - val_accuracy: 0.7684
Epoch 103/150
3/3 [==============================] - 0s 54ms/step - loss: 0.4179 - accuracy: 0.8311 - val_loss: 0.4679 - val_accuracy: 0.7789
Epoch 104/150
3/3 [==============================] - 0s 56ms/step - loss: 0.4377 - accuracy: 0.8047 - val_loss: 0.4682 - val_accuracy: 0.7895
Epoch 105/150
3/3 [==============================] - 0s 57ms/step - loss: 0.3824 - accuracy: 0.8364 - val_loss: 0.4683 - val_accuracy: 0.7895
Epoch 106/150
3/3 [==============================] - 0s 48ms/step - loss: 0.4247 - accuracy: 0.8232 - val_loss: 0.4685 - val_accuracy: 0.7895
Epoch 107/150
3/3 [==============================] - 0s 43ms/step - loss: 0.4139 - accuracy: 0.8259 - val_loss: 0.4685 - val_accuracy: 0.7895
Epoch 108/150
3/3 [==============================] - 0s 44ms/step - loss: 0.3931 - accuracy: 0.8259 - val_loss: 0.4688 - val_accuracy: 0.7895
Epoch 109/150
3/3 [==============================] - 0s 48ms/step - loss: 0.3599 - accuracy: 0.8470 - val_loss: 0.4689 - val_accuracy: 0.7895
Epoch 110/150
3/3 [==============================] - 0s 39ms/step - loss: 0.4084 - accuracy: 0.8311 - val_loss: 0.4692 - val_accuracy: 0.7895
Epoch 111/150
3/3 [==============================] - 0s 33ms/step - loss: 0.4389 - accuracy: 0.8100 - val_loss: 0.4698 - val_accuracy: 0.8000
Epoch 112/150
3/3 [==============================] - 0s 37ms/step - loss: 0.3772 - accuracy: 0.8654 - val_loss: 0.4701 - val_accuracy: 0.8000
Epoch 113/150
3/3 [==============================] - 0s 57ms/step - loss: 0.4123 - accuracy: 0.8100 - val_loss: 0.4704 - val_accuracy: 0.8000
Epoch 114/150
3/3 [==============================] - 0s 43ms/step - loss: 0.4111 - accuracy: 0.8364 - val_loss: 0.4708 - val_accuracy: 0.8000
Epoch 115/150
3/3 [==============================] - 0s 45ms/step - loss: 0.4390 - accuracy: 0.8206 - val_loss: 0.4712 - val_accuracy: 0.8000
Epoch 116/150
3/3 [==============================] - 0s 44ms/step - loss: 0.4156 - accuracy: 0.8074 - val_loss: 0.4715 - val_accuracy: 0.8000
Epoch 117/150
3/3 [==============================] - 0s 44ms/step - loss: 0.4179 - accuracy: 0.7995 - val_loss: 0.4717 - val_accuracy: 0.8000
Epoch 118/150
3/3 [==============================] - 0s 42ms/step - loss: 0.4216 - accuracy: 0.8100 - val_loss: 0.4717 - val_accuracy: 0.8000
Epoch 119/150
3/3 [==============================] - 0s 41ms/step - loss: 0.4212 - accuracy: 0.8206 - val_loss: 0.4720 - val_accuracy: 0.8000
Epoch 120/150
3/3 [==============================] - 0s 43ms/step - loss: 0.3908 - accuracy: 0.8311 - val_loss: 0.4721 - val_accuracy: 0.8000
Epoch 121/150
3/3 [==============================] - 0s 38ms/step - loss: 0.4061 - accuracy: 0.8259 - val_loss: 0.4723 - val_accuracy: 0.8000
Epoch 122/150
3/3 [==============================] - 0s 40ms/step - loss: 0.4152 - accuracy: 0.8232 - val_loss: 0.4725 - val_accuracy: 0.8000
Epoch 123/150
3/3 [==============================] - 0s 47ms/step - loss: 0.4118 - accuracy: 0.8074 - val_loss: 0.4726 - val_accuracy: 0.8000
Epoch 124/150
3/3 [==============================] - 0s 43ms/step - loss: 0.4242 - accuracy: 0.8153 - val_loss: 0.4726 - val_accuracy: 0.8000
Epoch 125/150
3/3 [==============================] - 0s 47ms/step - loss: 0.4075 - accuracy: 0.8285 - val_loss: 0.4726 - val_accuracy: 0.8000
Epoch 126/150
3/3 [==============================] - 0s 52ms/step - loss: 0.4122 - accuracy: 0.8232 - val_loss: 0.4729 - val_accuracy: 0.8000
Epoch 127/150
3/3 [==============================] - 0s 71ms/step - loss: 0.4161 - accuracy: 0.8206 - val_loss: 0.4734 - val_accuracy: 0.8000
Epoch 128/150
2/2 [==============================] - 0s 10ms/step - loss: 0.4221 - accuracy: 0.8439
3/3 [==============================] - 0s 71ms/step - loss: 0.3848 - accuracy: 0.8206 - val_loss: 0.4734 - val_accuracy: 0.8000
Epoch 129/150
3/3 [==============================] - 0s 71ms/step - loss: 0.4011 - accuracy: 0.8443 - val_loss: 0.4737 - val_accuracy: 0.8000
Epoch 130/150
3/3 [==============================] - 0s 77ms/step - loss: 0.4236 - accuracy: 0.8127 - val_loss: 0.4743 - val_accuracy: 0.8000
Epoch 131/150
3/3 [==============================] - 0s 86ms/step - loss: 0.4209 - accuracy: 0.8285 - val_loss: 0.4745 - val_accuracy: 0.8000
Epoch 132/150
1/3 [=========>....................] - ETA: 0s - loss: 0.3950 - accuracy: 0.8438Epoch 1/150
3/3 [==============================] - 0s 96ms/step - loss: 0.3920 - accuracy: 0.8232 - val_loss: 0.4743 - val_accuracy: 0.8000
Epoch 133/150
3/3 [==============================] - 0s 82ms/step - loss: 0.4004 - accuracy: 0.8206 - val_loss: 0.4742 - val_accuracy: 0.8000
Epoch 134/150
3/3 [==============================] - 0s 71ms/step - loss: 0.3998 - accuracy: 0.8179 - val_loss: 0.4745 - val_accuracy: 0.8000
Epoch 135/150
3/3 [==============================] - 0s 85ms/step - loss: 0.4116 - accuracy: 0.8232 - val_loss: 0.4743 - val_accuracy: 0.8000
Epoch 136/150
3/3 [==============================] - 0s 89ms/step - loss: 0.4047 - accuracy: 0.8443 - val_loss: 0.4744 - val_accuracy: 0.8000
Epoch 137/150
3/3 [==============================] - 0s 82ms/step - loss: 0.3816 - accuracy: 0.8047 - val_loss: 0.4747 - val_accuracy: 0.8000
Epoch 138/150
3/3 [==============================] - 0s 72ms/step - loss: 0.3908 - accuracy: 0.8364 - val_loss: 0.4750 - val_accuracy: 0.8000
Epoch 139/150
3/3 [==============================] - 0s 77ms/step - loss: 0.4111 - accuracy: 0.8311 - val_loss: 0.4751 - val_accuracy: 0.8000
Epoch 140/150
3/3 [==============================] - 0s 72ms/step - loss: 0.3986 - accuracy: 0.8206 - val_loss: 0.4754 - val_accuracy: 0.8000
Epoch 141/150
3/3 [==============================] - 0s 78ms/step - loss: 0.3787 - accuracy: 0.8417 - val_loss: 0.4759 - val_accuracy: 0.8000
Epoch 142/150
3/3 [==============================] - 0s 76ms/step - loss: 0.3401 - accuracy: 0.8654 - val_loss: 0.4765 - val_accuracy: 0.8000
Epoch 143/150
3/3 [==============================] - 0s 72ms/step - loss: 0.3979 - accuracy: 0.8470 - val_loss: 0.4768 - val_accuracy: 0.8000
Epoch 144/150
3/3 [==============================] - 0s 74ms/step - loss: 0.4083 - accuracy: 0.8127 - val_loss: 0.4770 - val_accuracy: 0.8000
Epoch 145/150
3/3 [==============================] - 0s 75ms/step - loss: 0.4043 - accuracy: 0.8153 - val_loss: 0.4770 - val_accuracy: 0.8000
Epoch 146/150
3/3 [==============================] - 0s 76ms/step - loss: 0.4379 - accuracy: 0.7995 - val_loss: 0.4775 - val_accuracy: 0.8000
Epoch 147/150
3/3 [==============================] - 0s 77ms/step - loss: 0.3752 - accuracy: 0.8522 - val_loss: 0.4777 - val_accuracy: 0.8000
Epoch 148/150
3/3 [==============================] - 0s 106ms/step - loss: 0.3779 - accuracy: 0.8443 - val_loss: 0.4779 - val_accuracy: 0.8000
Epoch 149/150
3/3 [==============================] - 0s 64ms/step - loss: 0.3689 - accuracy: 0.8496 - val_loss: 0.4780 - val_accuracy: 0.8000
Epoch 150/150
3/3 [==============================] - 0s 96ms/step - loss: 0.3658 - accuracy: 0.8628 - val_loss: 0.4783 - val_accuracy: 0.8000
2/2 [==============================] - 0s 20ms/step - loss: 0.5245 - accuracy: 0.7773
Epoch 1/150
3/3 [==============================] - 6s 504ms/step - loss: 0.7774 - accuracy: 0.5921 - val_loss: 0.6614 - val_accuracy: 0.6211
Epoch 2/150
3/3 [==============================] - 0s 78ms/step - loss: 0.7295 - accuracy: 0.6158 - val_loss: 0.6535 - val_accuracy: 0.6526
Epoch 3/150
3/3 [==============================] - 0s 91ms/step - loss: 0.7226 - accuracy: 0.6553 - val_loss: 0.6465 - val_accuracy: 0.6632
Epoch 4/150
3/3 [==============================] - 0s 83ms/step - loss: 0.6795 - accuracy: 0.6658 - val_loss: 0.6397 - val_accuracy: 0.6632
Epoch 5/150
3/3 [==============================] - 0s 83ms/step - loss: 0.6375 - accuracy: 0.6842 - val_loss: 0.6330 - val_accuracy: 0.6737
Epoch 6/150
3/3 [==============================] - 0s 70ms/step - loss: 0.6389 - accuracy: 0.6868 - val_loss: 0.6269 - val_accuracy: 0.6632
Epoch 7/150
3/3 [==============================] - 0s 70ms/step - loss: 0.6258 - accuracy: 0.6684 - val_loss: 0.6205 - val_accuracy: 0.6737
Epoch 8/150
3/3 [==============================] - 0s 64ms/step - loss: 0.6258 - accuracy: 0.6921 - val_loss: 0.6152 - val_accuracy: 0.6737
Epoch 9/150
3/3 [==============================] - 0s 81ms/step - loss: 0.5960 - accuracy: 0.6974 - val_loss: 0.6099 - val_accuracy: 0.6947
Epoch 10/150
3/3 [==============================] - 0s 90ms/step - loss: 0.5939 - accuracy: 0.7158 - val_loss: 0.6052 - val_accuracy: 0.7053
Epoch 11/150
3/3 [==============================] - 0s 93ms/step - loss: 0.5684 - accuracy: 0.7316 - val_loss: 0.6005 - val_accuracy: 0.7053
Epoch 12/150
3/3 [==============================] - 0s 77ms/step - loss: 0.5355 - accuracy: 0.7342 - val_loss: 0.5956 - val_accuracy: 0.6842
Epoch 13/150
3/3 [==============================] - 0s 74ms/step - loss: 0.6157 - accuracy: 0.6947 - val_loss: 0.5909 - val_accuracy: 0.6947
Epoch 14/150
3/3 [==============================] - 0s 76ms/step - loss: 0.5865 - accuracy: 0.7368 - val_loss: 0.5866 - val_accuracy: 0.7053
Epoch 15/150
3/3 [==============================] - 0s 68ms/step - loss: 0.5480 - accuracy: 0.7316 - val_loss: 0.5824 - val_accuracy: 0.6947
Epoch 16/150
3/3 [==============================] - 0s 89ms/step - loss: 0.5903 - accuracy: 0.7132 - val_loss: 0.5785 - val_accuracy: 0.7158
Epoch 17/150
3/3 [==============================] - 0s 74ms/step - loss: 0.5456 - accuracy: 0.7579 - val_loss: 0.5747 - val_accuracy: 0.7158
Epoch 18/150
3/3 [==============================] - 0s 84ms/step - loss: 0.5932 - accuracy: 0.7237 - val_loss: 0.5713 - val_accuracy: 0.7263
Epoch 19/150
3/3 [==============================] - 0s 74ms/step - loss: 0.5542 - accuracy: 0.7474 - val_loss: 0.5680 - val_accuracy: 0.7368
Epoch 20/150
3/3 [==============================] - 0s 71ms/step - loss: 0.5804 - accuracy: 0.7211 - val_loss: 0.5648 - val_accuracy: 0.7474
Epoch 21/150
3/3 [==============================] - 0s 83ms/step - loss: 0.5442 - accuracy: 0.7395 - val_loss: 0.5614 - val_accuracy: 0.7474
Epoch 22/150
3/3 [==============================] - 0s 67ms/step - loss: 0.5493 - accuracy: 0.7342 - val_loss: 0.5581 - val_accuracy: 0.7474
Epoch 23/150
3/3 [==============================] - 0s 80ms/step - loss: 0.5421 - accuracy: 0.7474 - val_loss: 0.5550 - val_accuracy: 0.7474
Epoch 24/150
3/3 [==============================] - 0s 72ms/step - loss: 0.5416 - accuracy: 0.7395 - val_loss: 0.5519 - val_accuracy: 0.7579
Epoch 25/150
3/3 [==============================] - 0s 76ms/step - loss: 0.5597 - accuracy: 0.7553 - val_loss: 0.5490 - val_accuracy: 0.7474
Epoch 26/150
3/3 [==============================] - 0s 72ms/step - loss: 0.5514 - accuracy: 0.7658 - val_loss: 0.5463 - val_accuracy: 0.7368
Epoch 27/150
3/3 [==============================] - 6s 545ms/step - loss: 0.9558 - accuracy: 0.4526 - val_loss: 0.7245 - val_accuracy: 0.3789
Epoch 2/150
3/3 [==============================] - 0s 80ms/step - loss: 0.5652 - accuracy: 0.7447 - val_loss: 0.5438 - val_accuracy: 0.7368
Epoch 28/150
3/3 [==============================] - 0s 81ms/step - loss: 0.9089 - accuracy: 0.4947 - val_loss: 0.7102 - val_accuracy: 0.4526
Epoch 3/150
3/3 [==============================] - 0s 69ms/step - loss: 0.5166 - accuracy: 0.7684 - val_loss: 0.5411 - val_accuracy: 0.7368
Epoch 29/150
3/3 [==============================] - 0s 88ms/step - loss: 0.9407 - accuracy: 0.4816 - val_loss: 0.6975 - val_accuracy: 0.4842
Epoch 4/150
3/3 [==============================] - 0s 88ms/step - loss: 0.5169 - accuracy: 0.7737 - val_loss: 0.5386 - val_accuracy: 0.7368
Epoch 30/150
3/3 [==============================] - 0s 69ms/step - loss: 0.8424 - accuracy: 0.5395 - val_loss: 0.6852 - val_accuracy: 0.6105
Epoch 5/150
3/3 [==============================] - 0s 68ms/step - loss: 0.5362 - accuracy: 0.7605 - val_loss: 0.5359 - val_accuracy: 0.7368
Epoch 31/150
3/3 [==============================] - 0s 77ms/step - loss: 0.8165 - accuracy: 0.5395 - val_loss: 0.6741 - val_accuracy: 0.6000
Epoch 6/150
3/3 [==============================] - 0s 67ms/step - loss: 0.5619 - accuracy: 0.7263 - val_loss: 0.5331 - val_accuracy: 0.7368
Epoch 32/150
3/3 [==============================] - 0s 64ms/step - loss: 0.7741 - accuracy: 0.5737 - val_loss: 0.6633 - val_accuracy: 0.6105
Epoch 7/150
3/3 [==============================] - 0s 72ms/step - loss: 0.5015 - accuracy: 0.7421 - val_loss: 0.5306 - val_accuracy: 0.7368
Epoch 33/150
3/3 [==============================] - 0s 76ms/step - loss: 0.7753 - accuracy: 0.5763 - val_loss: 0.6536 - val_accuracy: 0.6632
Epoch 8/150
3/3 [==============================] - 0s 77ms/step - loss: 0.5210 - accuracy: 0.7474 - val_loss: 0.5283 - val_accuracy: 0.7474
Epoch 34/150
3/3 [==============================] - 0s 70ms/step - loss: 0.7040 - accuracy: 0.6026 - val_loss: 0.6447 - val_accuracy: 0.7053
Epoch 9/150
3/3 [==============================] - 0s 101ms/step - loss: 0.5157 - accuracy: 0.7737 - val_loss: 0.5262 - val_accuracy: 0.7474
Epoch 35/150
3/3 [==============================] - 0s 79ms/step - loss: 0.7009 - accuracy: 0.6105 - val_loss: 0.6359 - val_accuracy: 0.7158
Epoch 10/150
3/3 [==============================] - 0s 81ms/step - loss: 0.5517 - accuracy: 0.7579 - val_loss: 0.5241 - val_accuracy: 0.7474
Epoch 36/150
3/3 [==============================] - 0s 77ms/step - loss: 0.6772 - accuracy: 0.6447 - val_loss: 0.6277 - val_accuracy: 0.7158
Epoch 11/150
3/3 [==============================] - 0s 83ms/step - loss: 0.5316 - accuracy: 0.7711 - val_loss: 0.5219 - val_accuracy: 0.7474
3/3 [==============================] - 0s 63ms/step - loss: 0.6946 - accuracy: 0.6237 - val_loss: 0.6201 - val_accuracy: 0.7263
Epoch 37/150
Epoch 12/150
3/3 [==============================] - 0s 61ms/step - loss: 0.5337 - accuracy: 0.7632 - val_loss: 0.5201 - val_accuracy: 0.7474
Epoch 38/150
3/3 [==============================] - 0s 67ms/step - loss: 0.6277 - accuracy: 0.6842 - val_loss: 0.6133 - val_accuracy: 0.7368
Epoch 13/150
3/3 [==============================] - 0s 71ms/step - loss: 0.6794 - accuracy: 0.6684 - val_loss: 0.6065 - val_accuracy: 0.7579
3/3 [==============================] - 0s 84ms/step - loss: 0.5214 - accuracy: 0.7553 - val_loss: 0.5185 - val_accuracy: 0.7474
Epoch 39/150
Epoch 14/150
3/3 [==============================] - 0s 66ms/step - loss: 0.5500 - accuracy: 0.7684 - val_loss: 0.5166 - val_accuracy: 0.7474
3/3 [==============================] - 0s 69ms/step - loss: 0.6133 - accuracy: 0.6868 - val_loss: 0.6002 - val_accuracy: 0.7579
Epoch 40/150
Epoch 15/150
3/3 [==============================] - 0s 99ms/step - loss: 0.4867 - accuracy: 0.7605 - val_loss: 0.5147 - val_accuracy: 0.7579
3/3 [==============================] - 0s 104ms/step - loss: 0.6165 - accuracy: 0.6947 - val_loss: 0.5941 - val_accuracy: 0.7579
Epoch 41/150
Epoch 16/150
3/3 [==============================] - 0s 72ms/step - loss: 0.5961 - accuracy: 0.7026 - val_loss: 0.5883 - val_accuracy: 0.7579
Epoch 17/150
3/3 [==============================] - 0s 105ms/step - loss: 0.4976 - accuracy: 0.7789 - val_loss: 0.5128 - val_accuracy: 0.7579
Epoch 42/150
3/3 [==============================] - 0s 78ms/step - loss: 0.6302 - accuracy: 0.6947 - val_loss: 0.5823 - val_accuracy: 0.7579
Epoch 18/150
3/3 [==============================] - 0s 69ms/step - loss: 0.5153 - accuracy: 0.7632 - val_loss: 0.5109 - val_accuracy: 0.7579
Epoch 43/150
3/3 [==============================] - 0s 52ms/step - loss: 0.5127 - accuracy: 0.7605 - val_loss: 0.5093 - val_accuracy: 0.7579
3/3 [==============================] - 0s 72ms/step - loss: 0.6164 - accuracy: 0.7000 - val_loss: 0.5773 - val_accuracy: 0.7368
Epoch 19/150
Epoch 44/150
3/3 [==============================] - 0s 75ms/step - loss: 0.6039 - accuracy: 0.6974 - val_loss: 0.5722 - val_accuracy: 0.7368
Epoch 20/150
3/3 [==============================] - 0s 68ms/step - loss: 0.5240 - accuracy: 0.7711 - val_loss: 0.5075 - val_accuracy: 0.7474
Epoch 45/150
3/3 [==============================] - 0s 64ms/step - loss: 0.6139 - accuracy: 0.7079 - val_loss: 0.5675 - val_accuracy: 0.7368
Epoch 21/150
3/3 [==============================] - 0s 70ms/step - loss: 0.5052 - accuracy: 0.7763 - val_loss: 0.5061 - val_accuracy: 0.7474
Epoch 46/150
3/3 [==============================] - 0s 59ms/step - loss: 0.4928 - accuracy: 0.7763 - val_loss: 0.5046 - val_accuracy: 0.7474
3/3 [==============================] - 0s 71ms/step - loss: 0.6179 - accuracy: 0.7132 - val_loss: 0.5629 - val_accuracy: 0.7474
Epoch 22/150
Epoch 47/150
3/3 [==============================] - 0s 92ms/step - loss: 0.4941 - accuracy: 0.7789 - val_loss: 0.5033 - val_accuracy: 0.7474
3/3 [==============================] - 0s 106ms/step - loss: 0.5935 - accuracy: 0.7132 - val_loss: 0.5583 - val_accuracy: 0.7474
Epoch 48/150
Epoch 23/150
3/3 [==============================] - 0s 55ms/step - loss: 0.5805 - accuracy: 0.7263 - val_loss: 0.5540 - val_accuracy: 0.7474
Epoch 24/150
3/3 [==============================] - 0s 82ms/step - loss: 0.5128 - accuracy: 0.7658 - val_loss: 0.5016 - val_accuracy: 0.7474
Epoch 49/150
3/3 [==============================] - 0s 84ms/step - loss: 0.6309 - accuracy: 0.6974 - val_loss: 0.5500 - val_accuracy: 0.7474
Epoch 25/150
3/3 [==============================] - 0s 70ms/step - loss: 0.5380 - accuracy: 0.7474 - val_loss: 0.5000 - val_accuracy: 0.7474
Epoch 50/150
3/3 [==============================] - 0s 82ms/step - loss: 0.5795 - accuracy: 0.7158 - val_loss: 0.5459 - val_accuracy: 0.7474
Epoch 26/150
3/3 [==============================] - 0s 82ms/step - loss: 0.5061 - accuracy: 0.7895 - val_loss: 0.4984 - val_accuracy: 0.7474
Epoch 51/150
3/3 [==============================] - 0s 74ms/step - loss: 0.4798 - accuracy: 0.7895 - val_loss: 0.4968 - val_accuracy: 0.7579
Epoch 52/150
3/3 [==============================] - 0s 107ms/step - loss: 0.5852 - accuracy: 0.7368 - val_loss: 0.5418 - val_accuracy: 0.7474
Epoch 27/150
3/3 [==============================] - 0s 61ms/step - loss: 0.5223 - accuracy: 0.7579 - val_loss: 0.4953 - val_accuracy: 0.7579
Epoch 53/150
3/3 [==============================] - 0s 78ms/step - loss: 0.5677 - accuracy: 0.7316 - val_loss: 0.5376 - val_accuracy: 0.7579
Epoch 28/150
3/3 [==============================] - 0s 74ms/step - loss: 0.5119 - accuracy: 0.7684 - val_loss: 0.4936 - val_accuracy: 0.7579
Epoch 54/150
3/3 [==============================] - 0s 75ms/step - loss: 0.5985 - accuracy: 0.7316 - val_loss: 0.5337 - val_accuracy: 0.7579
Epoch 29/150
3/3 [==============================] - 0s 65ms/step - loss: 0.4662 - accuracy: 0.8026 - val_loss: 0.4924 - val_accuracy: 0.7579
Epoch 55/150
3/3 [==============================] - 0s 71ms/step - loss: 0.6229 - accuracy: 0.6921 - val_loss: 0.5298 - val_accuracy: 0.7579
Epoch 30/150
3/3 [==============================] - 0s 63ms/step - loss: 0.4544 - accuracy: 0.8026 - val_loss: 0.4912 - val_accuracy: 0.7684
Epoch 56/150
3/3 [==============================] - 0s 69ms/step - loss: 0.5275 - accuracy: 0.7605 - val_loss: 0.5262 - val_accuracy: 0.7579
Epoch 31/150
3/3 [==============================] - 0s 68ms/step - loss: 0.5197 - accuracy: 0.7526 - val_loss: 0.4899 - val_accuracy: 0.7684
Epoch 57/150
3/3 [==============================] - 0s 69ms/step - loss: 0.5614 - accuracy: 0.7316 - val_loss: 0.5225 - val_accuracy: 0.7684
Epoch 32/150
3/3 [==============================] - 0s 45ms/step - loss: 0.4719 - accuracy: 0.7842 - val_loss: 0.4885 - val_accuracy: 0.7684
Epoch 58/150
3/3 [==============================] - 0s 65ms/step - loss: 0.4940 - accuracy: 0.7868 - val_loss: 0.4874 - val_accuracy: 0.7684
3/3 [==============================] - 0s 65ms/step - loss: 0.5749 - accuracy: 0.7105 - val_loss: 0.5187 - val_accuracy: 0.7684
Epoch 33/150
Epoch 59/150
3/3 [==============================] - 0s 58ms/step - loss: 0.4907 - accuracy: 0.7658 - val_loss: 0.4863 - val_accuracy: 0.7684
Epoch 60/150
3/3 [==============================] - 0s 64ms/step - loss: 0.5942 - accuracy: 0.7237 - val_loss: 0.5150 - val_accuracy: 0.7684
Epoch 34/150
3/3 [==============================] - 0s 69ms/step - loss: 0.5123 - accuracy: 0.7868 - val_loss: 0.4853 - val_accuracy: 0.7684
Epoch 61/150
3/3 [==============================] - 0s 71ms/step - loss: 0.5580 - accuracy: 0.7211 - val_loss: 0.5114 - val_accuracy: 0.7684
Epoch 35/150
3/3 [==============================] - 0s 71ms/step - loss: 0.4606 - accuracy: 0.7895 - val_loss: 0.4844 - val_accuracy: 0.7684
Epoch 62/150
3/3 [==============================] - 0s 69ms/step - loss: 0.5535 - accuracy: 0.7526 - val_loss: 0.5081 - val_accuracy: 0.7684
Epoch 36/150
3/3 [==============================] - 0s 59ms/step - loss: 0.5736 - accuracy: 0.7395 - val_loss: 0.5048 - val_accuracy: 0.7684
3/3 [==============================] - 0s 71ms/step - loss: 0.4850 - accuracy: 0.7658 - val_loss: 0.4836 - val_accuracy: 0.7684
Epoch 63/150
Epoch 37/150
3/3 [==============================] - 0s 69ms/step - loss: 0.4739 - accuracy: 0.8026 - val_loss: 0.4827 - val_accuracy: 0.7684
Epoch 64/150
3/3 [==============================] - 0s 66ms/step - loss: 0.5528 - accuracy: 0.7500 - val_loss: 0.5016 - val_accuracy: 0.7684
Epoch 38/150
3/3 [==============================] - 0s 54ms/step - loss: 0.4708 - accuracy: 0.7895 - val_loss: 0.4819 - val_accuracy: 0.7684
3/3 [==============================] - 0s 58ms/step - loss: 0.5668 - accuracy: 0.7316 - val_loss: 0.4983 - val_accuracy: 0.7684
Epoch 39/150
Epoch 65/150
3/3 [==============================] - 0s 66ms/step - loss: 0.4728 - accuracy: 0.8026 - val_loss: 0.4808 - val_accuracy: 0.7684
Epoch 66/150
3/3 [==============================] - 0s 64ms/step - loss: 0.5418 - accuracy: 0.7342 - val_loss: 0.4950 - val_accuracy: 0.7895
Epoch 40/150
3/3 [==============================] - 0s 63ms/step - loss: 0.6147 - accuracy: 0.6947 - val_loss: 0.4913 - val_accuracy: 0.7895
Epoch 41/150
3/3 [==============================] - 0s 76ms/step - loss: 0.4663 - accuracy: 0.8079 - val_loss: 0.4799 - val_accuracy: 0.7684
Epoch 67/150
3/3 [==============================] - 0s 73ms/step - loss: 0.5750 - accuracy: 0.7500 - val_loss: 0.4881 - val_accuracy: 0.7895
Epoch 42/150
3/3 [==============================] - 0s 62ms/step - loss: 0.4624 - accuracy: 0.8079 - val_loss: 0.4792 - val_accuracy: 0.7684
Epoch 68/150
3/3 [==============================] - 0s 66ms/step - loss: 0.5644 - accuracy: 0.7500 - val_loss: 0.4850 - val_accuracy: 0.7895
3/3 [==============================] - 0s 73ms/step - loss: 0.5039 - accuracy: 0.7737 - val_loss: 0.4783 - val_accuracy: 0.7684
Epoch 69/150
Epoch 43/150
3/3 [==============================] - 0s 94ms/step - loss: 0.5179 - accuracy: 0.7763 - val_loss: 0.4777 - val_accuracy: 0.7684
Epoch 70/150
3/3 [==============================] - 0s 102ms/step - loss: 0.5014 - accuracy: 0.7684 - val_loss: 0.4818 - val_accuracy: 0.7895
1/3 [=========>....................] - ETA: 0s - loss: 0.5523 - accuracy: 0.7500Epoch 44/150
3/3 [==============================] - 0s 97ms/step - loss: 0.4866 - accuracy: 0.7737 - val_loss: 0.4772 - val_accuracy: 0.7684
Epoch 71/150
3/3 [==============================] - 0s 111ms/step - loss: 0.5344 - accuracy: 0.7789 - val_loss: 0.4785 - val_accuracy: 0.8000
Epoch 45/150
3/3 [==============================] - 0s 69ms/step - loss: 0.5675 - accuracy: 0.7500 - val_loss: 0.4753 - val_accuracy: 0.8000
Epoch 46/150
3/3 [==============================] - 0s 108ms/step - loss: 0.4835 - accuracy: 0.7895 - val_loss: 0.4767 - val_accuracy: 0.7684
Epoch 72/150
3/3 [==============================] - 0s 95ms/step - loss: 0.4922 - accuracy: 0.7868 - val_loss: 0.4759 - val_accuracy: 0.7684
Epoch 73/150
3/3 [==============================] - 0s 109ms/step - loss: 0.5605 - accuracy: 0.7447 - val_loss: 0.4724 - val_accuracy: 0.8000
Epoch 47/150
3/3 [==============================] - 0s 66ms/step - loss: 0.5210 - accuracy: 0.7500 - val_loss: 0.4695 - val_accuracy: 0.8000
Epoch 48/150
3/3 [==============================] - 0s 75ms/step - loss: 0.5262 - accuracy: 0.7526 - val_loss: 0.4753 - val_accuracy: 0.7684
Epoch 74/150
3/3 [==============================] - 0s 53ms/step - loss: 0.5027 - accuracy: 0.7500 - val_loss: 0.4666 - val_accuracy: 0.8000
Epoch 49/150
3/3 [==============================] - 0s 66ms/step - loss: 0.4895 - accuracy: 0.7868 - val_loss: 0.4746 - val_accuracy: 0.7789
Epoch 75/150
3/3 [==============================] - 0s 58ms/step - loss: 0.5273 - accuracy: 0.7474 - val_loss: 0.4639 - val_accuracy: 0.8105
Epoch 50/150
3/3 [==============================] - 0s 66ms/step - loss: 0.5014 - accuracy: 0.7605 - val_loss: 0.4739 - val_accuracy: 0.7789
Epoch 76/150
3/3 [==============================] - 0s 65ms/step - loss: 0.4901 - accuracy: 0.7632 - val_loss: 0.4611 - val_accuracy: 0.8105
3/3 [==============================] - 0s 71ms/step - loss: 0.5144 - accuracy: 0.7868 - val_loss: 0.4733 - val_accuracy: 0.7789
Epoch 77/150
Epoch 51/150
3/3 [==============================] - 0s 71ms/step - loss: 0.5017 - accuracy: 0.7632 - val_loss: 0.4588 - val_accuracy: 0.8211
3/3 [==============================] - 0s 69ms/step - loss: 0.4746 - accuracy: 0.7868 - val_loss: 0.4726 - val_accuracy: 0.7789
Epoch 52/150
Epoch 78/150
3/3 [==============================] - 0s 62ms/step - loss: 0.4586 - accuracy: 0.8184 - val_loss: 0.4723 - val_accuracy: 0.7789
3/3 [==============================] - 0s 64ms/step - loss: 0.5077 - accuracy: 0.7737 - val_loss: 0.4563 - val_accuracy: 0.8211
Epoch 53/150
Epoch 79/150
3/3 [==============================] - 0s 61ms/step - loss: 0.5365 - accuracy: 0.7500 - val_loss: 0.4536 - val_accuracy: 0.8211
Epoch 54/150
3/3 [==============================] - 0s 66ms/step - loss: 0.4911 - accuracy: 0.8000 - val_loss: 0.4719 - val_accuracy: 0.7789
Epoch 80/150
3/3 [==============================] - 0s 60ms/step - loss: 0.5187 - accuracy: 0.7789 - val_loss: 0.4513 - val_accuracy: 0.8211
Epoch 55/150
3/3 [==============================] - 0s 65ms/step - loss: 0.4778 - accuracy: 0.7974 - val_loss: 0.4717 - val_accuracy: 0.7789
Epoch 81/150
3/3 [==============================] - 0s 61ms/step - loss: 0.5057 - accuracy: 0.7763 - val_loss: 0.4712 - val_accuracy: 0.7789
3/3 [==============================] - 0s 68ms/step - loss: 0.5046 - accuracy: 0.7605 - val_loss: 0.4489 - val_accuracy: 0.8316
Epoch 56/150
Epoch 82/150
3/3 [==============================] - 0s 62ms/step - loss: 0.4861 - accuracy: 0.7684 - val_loss: 0.4706 - val_accuracy: 0.7789
Epoch 83/150
3/3 [==============================] - 0s 70ms/step - loss: 0.5603 - accuracy: 0.7447 - val_loss: 0.4466 - val_accuracy: 0.8316
Epoch 57/150
3/3 [==============================] - 0s 71ms/step - loss: 0.4892 - accuracy: 0.7789 - val_loss: 0.4701 - val_accuracy: 0.7789
Epoch 84/150
3/3 [==============================] - 0s 69ms/step - loss: 0.5245 - accuracy: 0.7632 - val_loss: 0.4446 - val_accuracy: 0.8316
Epoch 58/150
3/3 [==============================] - 0s 67ms/step - loss: 0.5041 - accuracy: 0.7605 - val_loss: 0.4426 - val_accuracy: 0.8316
Epoch 59/150
3/3 [==============================] - 0s 66ms/step - loss: 0.4960 - accuracy: 0.7816 - val_loss: 0.4696 - val_accuracy: 0.7789
Epoch 85/150
3/3 [==============================] - 0s 65ms/step - loss: 0.4461 - accuracy: 0.8053 - val_loss: 0.4693 - val_accuracy: 0.7789
Epoch 86/150
3/3 [==============================] - 0s 75ms/step - loss: 0.5252 - accuracy: 0.7789 - val_loss: 0.4407 - val_accuracy: 0.8316
Epoch 60/150
3/3 [==============================] - 0s 66ms/step - loss: 0.4671 - accuracy: 0.7632 - val_loss: 0.4688 - val_accuracy: 0.7789
Epoch 87/150
3/3 [==============================] - 0s 77ms/step - loss: 0.4658 - accuracy: 0.8000 - val_loss: 0.4388 - val_accuracy: 0.8316
Epoch 61/150
3/3 [==============================] - 0s 60ms/step - loss: 0.4811 - accuracy: 0.7789 - val_loss: 0.4684 - val_accuracy: 0.7789
3/3 [==============================] - 0s 60ms/step - loss: 0.4796 - accuracy: 0.7737 - val_loss: 0.4369 - val_accuracy: 0.8316
Epoch 88/150
Epoch 62/150
3/3 [==============================] - 0s 64ms/step - loss: 0.4858 - accuracy: 0.7684 - val_loss: 0.4679 - val_accuracy: 0.7789
3/3 [==============================] - 0s 59ms/step - loss: 0.5691 - accuracy: 0.7526 - val_loss: 0.4348 - val_accuracy: 0.8316
Epoch 63/150
Epoch 89/150
3/3 [==============================] - 0s 64ms/step - loss: 0.4649 - accuracy: 0.8053 - val_loss: 0.4328 - val_accuracy: 0.8316
Epoch 64/150
3/3 [==============================] - 0s 65ms/step - loss: 0.5184 - accuracy: 0.7868 - val_loss: 0.4676 - val_accuracy: 0.7789
Epoch 90/150
3/3 [==============================] - 0s 57ms/step - loss: 0.5113 - accuracy: 0.7868 - val_loss: 0.4309 - val_accuracy: 0.8316
Epoch 65/150
3/3 [==============================] - 0s 66ms/step - loss: 0.4591 - accuracy: 0.7868 - val_loss: 0.4672 - val_accuracy: 0.7789
Epoch 91/150
3/3 [==============================] - 0s 68ms/step - loss: 0.4761 - accuracy: 0.7632 - val_loss: 0.4289 - val_accuracy: 0.8316
Epoch 66/150
3/3 [==============================] - 0s 65ms/step - loss: 0.5251 - accuracy: 0.7763 - val_loss: 0.4669 - val_accuracy: 0.7789
Epoch 92/150
3/3 [==============================] - 0s 92ms/step - loss: 0.4820 - accuracy: 0.7553 - val_loss: 0.4667 - val_accuracy: 0.7789
3/3 [==============================] - 0s 99ms/step - loss: 0.5339 - accuracy: 0.7605 - val_loss: 0.4268 - val_accuracy: 0.8316
Epoch 67/150
Epoch 93/150
3/3 [==============================] - 0s 74ms/step - loss: 0.4855 - accuracy: 0.7921 - val_loss: 0.4248 - val_accuracy: 0.8316
Epoch 68/150
3/3 [==============================] - 0s 115ms/step - loss: 0.4829 - accuracy: 0.7789 - val_loss: 0.4661 - val_accuracy: 0.7789
Epoch 94/150
3/3 [==============================] - 0s 103ms/step - loss: 0.5092 - accuracy: 0.7658 - val_loss: 0.4229 - val_accuracy: 0.8421
Epoch 69/150
3/3 [==============================] - 0s 86ms/step - loss: 0.4518 - accuracy: 0.7974 - val_loss: 0.4657 - val_accuracy: 0.7789
Epoch 95/150
3/3 [==============================] - 0s 84ms/step - loss: 0.5048 - accuracy: 0.7684 - val_loss: 0.4208 - val_accuracy: 0.8526
Epoch 70/150
3/3 [==============================] - 0s 84ms/step - loss: 0.4674 - accuracy: 0.7816 - val_loss: 0.4652 - val_accuracy: 0.7789
Epoch 96/150
3/3 [==============================] - 0s 86ms/step - loss: 0.5008 - accuracy: 0.7658 - val_loss: 0.4189 - val_accuracy: 0.8526
Epoch 71/150
3/3 [==============================] - 0s 76ms/step - loss: 0.4569 - accuracy: 0.7868 - val_loss: 0.4650 - val_accuracy: 0.7789
Epoch 97/150
3/3 [==============================] - 0s 85ms/step - loss: 0.5267 - accuracy: 0.7632 - val_loss: 0.4170 - val_accuracy: 0.8526
Epoch 72/150
3/3 [==============================] - 0s 81ms/step - loss: 0.4850 - accuracy: 0.7763 - val_loss: 0.4648 - val_accuracy: 0.7789
Epoch 98/150
3/3 [==============================] - 0s 75ms/step - loss: 0.5210 - accuracy: 0.7763 - val_loss: 0.4154 - val_accuracy: 0.8526
Epoch 73/150
3/3 [==============================] - 0s 77ms/step - loss: 0.4374 - accuracy: 0.7947 - val_loss: 0.4646 - val_accuracy: 0.7789
Epoch 99/150
3/3 [==============================] - 0s 53ms/step - loss: 0.4634 - accuracy: 0.7816 - val_loss: 0.4643 - val_accuracy: 0.7789
Epoch 100/150
3/3 [==============================] - 0s 103ms/step - loss: 0.5421 - accuracy: 0.7526 - val_loss: 0.4137 - val_accuracy: 0.8526
Epoch 74/150
3/3 [==============================] - 0s 80ms/step - loss: 0.4348 - accuracy: 0.8132 - val_loss: 0.4642 - val_accuracy: 0.7789
Epoch 101/150
3/3 [==============================] - 0s 95ms/step - loss: 0.4976 - accuracy: 0.7579 - val_loss: 0.4120 - val_accuracy: 0.8526
Epoch 75/150
3/3 [==============================] - 0s 92ms/step - loss: 0.4462 - accuracy: 0.7974 - val_loss: 0.4640 - val_accuracy: 0.7789
Epoch 102/150
3/3 [==============================] - 0s 104ms/step - loss: 0.4969 - accuracy: 0.7895 - val_loss: 0.4103 - val_accuracy: 0.8526
Epoch 76/150
3/3 [==============================] - 0s 92ms/step - loss: 0.4801 - accuracy: 0.8026 - val_loss: 0.4636 - val_accuracy: 0.7789
Epoch 103/150
3/3 [==============================] - 0s 109ms/step - loss: 0.4865 - accuracy: 0.7684 - val_loss: 0.4086 - val_accuracy: 0.8632
Epoch 77/150
3/3 [==============================] - 0s 96ms/step - loss: 0.4355 - accuracy: 0.8132 - val_loss: 0.4631 - val_accuracy: 0.7789
Epoch 104/150
3/3 [==============================] - 0s 80ms/step - loss: 0.5131 - accuracy: 0.7842 - val_loss: 0.4070 - val_accuracy: 0.8632
Epoch 78/150
3/3 [==============================] - 0s 87ms/step - loss: 0.4753 - accuracy: 0.7974 - val_loss: 0.4627 - val_accuracy: 0.7789
Epoch 105/150
3/3 [==============================] - 0s 81ms/step - loss: 0.5189 - accuracy: 0.7763 - val_loss: 0.4055 - val_accuracy: 0.8632
Epoch 79/150
3/3 [==============================] - 0s 61ms/step - loss: 0.4716 - accuracy: 0.7974 - val_loss: 0.4623 - val_accuracy: 0.7789
Epoch 106/150
3/3 [==============================] - 0s 72ms/step - loss: 0.5237 - accuracy: 0.7553 - val_loss: 0.4042 - val_accuracy: 0.8632
Epoch 80/150
3/3 [==============================] - 0s 81ms/step - loss: 0.4814 - accuracy: 0.7816 - val_loss: 0.4622 - val_accuracy: 0.7789
Epoch 107/150
3/3 [==============================] - 0s 63ms/step - loss: 0.4742 - accuracy: 0.7763 - val_loss: 0.4027 - val_accuracy: 0.8632
Epoch 81/150
3/3 [==============================] - 0s 74ms/step - loss: 0.5184 - accuracy: 0.7789 - val_loss: 0.4011 - val_accuracy: 0.8737
3/3 [==============================] - 0s 118ms/step - loss: 0.4926 - accuracy: 0.8000 - val_loss: 0.4620 - val_accuracy: 0.7789
Epoch 82/150
Epoch 108/150
3/3 [==============================] - 0s 63ms/step - loss: 0.4900 - accuracy: 0.7684 - val_loss: 0.3998 - val_accuracy: 0.8737
Epoch 83/150
3/3 [==============================] - 0s 72ms/step - loss: 0.4423 - accuracy: 0.8053 - val_loss: 0.4621 - val_accuracy: 0.7789
1/3 [=========>....................] - ETA: 0s - loss: 0.4786 - accuracy: 0.7969Epoch 109/150
3/3 [==============================] - 0s 71ms/step - loss: 0.4950 - accuracy: 0.7579 - val_loss: 0.3985 - val_accuracy: 0.8737
Epoch 84/150
3/3 [==============================] - 0s 74ms/step - loss: 0.4635 - accuracy: 0.7974 - val_loss: 0.4621 - val_accuracy: 0.7789
Epoch 110/150
3/3 [==============================] - 0s 88ms/step - loss: 0.4919 - accuracy: 0.7763 - val_loss: 0.3971 - val_accuracy: 0.8737
Epoch 85/150
3/3 [==============================] - 0s 84ms/step - loss: 0.4683 - accuracy: 0.7921 - val_loss: 0.4622 - val_accuracy: 0.7789
Epoch 111/150
3/3 [==============================] - 0s 73ms/step - loss: 0.5022 - accuracy: 0.7711 - val_loss: 0.3960 - val_accuracy: 0.8632
Epoch 86/150
3/3 [==============================] - 0s 81ms/step - loss: 0.4786 - accuracy: 0.8105 - val_loss: 0.4623 - val_accuracy: 0.7789
Epoch 112/150
3/3 [==============================] - 0s 78ms/step - loss: 0.5457 - accuracy: 0.7553 - val_loss: 0.3948 - val_accuracy: 0.8632
Epoch 87/150
3/3 [==============================] - 0s 81ms/step - loss: 0.4698 - accuracy: 0.7842 - val_loss: 0.4623 - val_accuracy: 0.7895
Epoch 113/150
3/3 [==============================] - 0s 77ms/step - loss: 0.5318 - accuracy: 0.7500 - val_loss: 0.3935 - val_accuracy: 0.8737
Epoch 88/150
3/3 [==============================] - 0s 82ms/step - loss: 0.4674 - accuracy: 0.7921 - val_loss: 0.4622 - val_accuracy: 0.7895
Epoch 114/150
3/3 [==============================] - 0s 94ms/step - loss: 0.5486 - accuracy: 0.7447 - val_loss: 0.3925 - val_accuracy: 0.8737
Epoch 89/150
3/3 [==============================] - 0s 88ms/step - loss: 0.4363 - accuracy: 0.8079 - val_loss: 0.4623 - val_accuracy: 0.7895
Epoch 115/150
3/3 [==============================] - 0s 86ms/step - loss: 0.4967 - accuracy: 0.7868 - val_loss: 0.3915 - val_accuracy: 0.8737
Epoch 90/150
3/3 [==============================] - 0s 99ms/step - loss: 0.4384 - accuracy: 0.8211 - val_loss: 0.4624 - val_accuracy: 0.7895
Epoch 116/150
3/3 [==============================] - 0s 97ms/step - loss: 0.4912 - accuracy: 0.7658 - val_loss: 0.3904 - val_accuracy: 0.8737
Epoch 91/150
3/3 [==============================] - 0s 101ms/step - loss: 0.4555 - accuracy: 0.8000 - val_loss: 0.4624 - val_accuracy: 0.7895
Epoch 117/150
3/3 [==============================] - 0s 85ms/step - loss: 0.4936 - accuracy: 0.7868 - val_loss: 0.3895 - val_accuracy: 0.8737
Epoch 92/150
3/3 [==============================] - 0s 72ms/step - loss: 0.4244 - accuracy: 0.8158 - val_loss: 0.4625 - val_accuracy: 0.7895
Epoch 118/150
3/3 [==============================] - 0s 88ms/step - loss: 0.4929 - accuracy: 0.7842 - val_loss: 0.3886 - val_accuracy: 0.8737
Epoch 93/150
3/3 [==============================] - 0s 88ms/step - loss: 0.4501 - accuracy: 0.7895 - val_loss: 0.4627 - val_accuracy: 0.7895
Epoch 119/150
3/3 [==============================] - 0s 101ms/step - loss: 0.4799 - accuracy: 0.7816 - val_loss: 0.3876 - val_accuracy: 0.8737
Epoch 94/150
3/3 [==============================] - 0s 72ms/step - loss: 0.4595 - accuracy: 0.8000 - val_loss: 0.4627 - val_accuracy: 0.7895
1/3 [=========>....................] - ETA: 0s - loss: 0.4816 - accuracy: 0.7656Epoch 120/150
3/3 [==============================] - 0s 111ms/step - loss: 0.5312 - accuracy: 0.7632 - val_loss: 0.3866 - val_accuracy: 0.8737
Epoch 95/150
3/3 [==============================] - 0s 107ms/step - loss: 0.5017 - accuracy: 0.7868 - val_loss: 0.4626 - val_accuracy: 0.7895
Epoch 121/150
3/3 [==============================] - 0s 80ms/step - loss: 0.4660 - accuracy: 0.7842 - val_loss: 0.3858 - val_accuracy: 0.8737
Epoch 96/150
3/3 [==============================] - 0s 76ms/step - loss: 0.4573 - accuracy: 0.7868 - val_loss: 0.4626 - val_accuracy: 0.7895
Epoch 122/150
3/3 [==============================] - 0s 93ms/step - loss: 0.4738 - accuracy: 0.7921 - val_loss: 0.3848 - val_accuracy: 0.8737
Epoch 97/150
3/3 [==============================] - 0s 102ms/step - loss: 0.4489 - accuracy: 0.8263 - val_loss: 0.4622 - val_accuracy: 0.7895
Epoch 123/150
3/3 [==============================] - 0s 103ms/step - loss: 0.4666 - accuracy: 0.7895 - val_loss: 0.3842 - val_accuracy: 0.8737
Epoch 98/150
3/3 [==============================] - 0s 102ms/step - loss: 0.4593 - accuracy: 0.8053 - val_loss: 0.4622 - val_accuracy: 0.7895
Epoch 124/150
3/3 [==============================] - 0s 94ms/step - loss: 0.4531 - accuracy: 0.8026 - val_loss: 0.3834 - val_accuracy: 0.8737
Epoch 99/150
3/3 [==============================] - 0s 102ms/step - loss: 0.4447 - accuracy: 0.8053 - val_loss: 0.4621 - val_accuracy: 0.7895
Epoch 125/150
3/3 [==============================] - 0s 106ms/step - loss: 0.4741 - accuracy: 0.7921 - val_loss: 0.3824 - val_accuracy: 0.8737
Epoch 100/150
3/3 [==============================] - 0s 81ms/step - loss: 0.4718 - accuracy: 0.8053 - val_loss: 0.4622 - val_accuracy: 0.7895
Epoch 126/150
3/3 [==============================] - 0s 72ms/step - loss: 0.4560 - accuracy: 0.7974 - val_loss: 0.3816 - val_accuracy: 0.8737
Epoch 101/150
3/3 [==============================] - 0s 86ms/step - loss: 0.4578 - accuracy: 0.7921 - val_loss: 0.4622 - val_accuracy: 0.7895
Epoch 127/150
3/3 [==============================] - 0s 71ms/step - loss: 0.4925 - accuracy: 0.8026 - val_loss: 0.3809 - val_accuracy: 0.8737
Epoch 102/150
3/3 [==============================] - 0s 85ms/step - loss: 0.4440 - accuracy: 0.8026 - val_loss: 0.4620 - val_accuracy: 0.7895
Epoch 128/150
3/3 [==============================] - 0s 63ms/step - loss: 0.4417 - accuracy: 0.8395 - val_loss: 0.4619 - val_accuracy: 0.7895
3/3 [==============================] - 0s 104ms/step - loss: 0.4676 - accuracy: 0.7895 - val_loss: 0.3802 - val_accuracy: 0.8737
Epoch 103/150
Epoch 129/150
3/3 [==============================] - 0s 81ms/step - loss: 0.5097 - accuracy: 0.7789 - val_loss: 0.3794 - val_accuracy: 0.8737
Epoch 104/150
3/3 [==============================] - 0s 111ms/step - loss: 0.4812 - accuracy: 0.7868 - val_loss: 0.4619 - val_accuracy: 0.7895
Epoch 130/150
3/3 [==============================] - 0s 75ms/step - loss: 0.4586 - accuracy: 0.8000 - val_loss: 0.3786 - val_accuracy: 0.8737
Epoch 105/150
3/3 [==============================] - 0s 86ms/step - loss: 0.4612 - accuracy: 0.7947 - val_loss: 0.4617 - val_accuracy: 0.7895
Epoch 131/150
3/3 [==============================] - 0s 90ms/step - loss: 0.4610 - accuracy: 0.7868 - val_loss: 0.3776 - val_accuracy: 0.8737
Epoch 106/150
3/3 [==============================] - 0s 91ms/step - loss: 0.4511 - accuracy: 0.8132 - val_loss: 0.4617 - val_accuracy: 0.7895
Epoch 132/150
3/3 [==============================] - 0s 99ms/step - loss: 0.4331 - accuracy: 0.8000 - val_loss: 0.3769 - val_accuracy: 0.8737
Epoch 107/150
3/3 [==============================] - 0s 98ms/step - loss: 0.4522 - accuracy: 0.7974 - val_loss: 0.4617 - val_accuracy: 0.7895
Epoch 133/150
3/3 [==============================] - 0s 81ms/step - loss: 0.4499 - accuracy: 0.8289 - val_loss: 0.3762 - val_accuracy: 0.8842
Epoch 108/150
3/3 [==============================] - 0s 76ms/step - loss: 0.4276 - accuracy: 0.8132 - val_loss: 0.4618 - val_accuracy: 0.7895
Epoch 134/150
3/3 [==============================] - 0s 87ms/step - loss: 0.5021 - accuracy: 0.7711 - val_loss: 0.3754 - val_accuracy: 0.8737
Epoch 109/150
3/3 [==============================] - 0s 84ms/step - loss: 0.4441 - accuracy: 0.8079 - val_loss: 0.4619 - val_accuracy: 0.7895
Epoch 135/150
3/3 [==============================] - 0s 81ms/step - loss: 0.4858 - accuracy: 0.7684 - val_loss: 0.3745 - val_accuracy: 0.8737
Epoch 110/150
3/3 [==============================] - 0s 79ms/step - loss: 0.4497 - accuracy: 0.8211 - val_loss: 0.4620 - val_accuracy: 0.7895
Epoch 136/150
3/3 [==============================] - 0s 74ms/step - loss: 0.5580 - accuracy: 0.7763 - val_loss: 0.3739 - val_accuracy: 0.8737
Epoch 111/150
3/3 [==============================] - 0s 100ms/step - loss: 0.4662 - accuracy: 0.8053 - val_loss: 0.4623 - val_accuracy: 0.7895
Epoch 137/150
3/3 [==============================] - 0s 101ms/step - loss: 0.4637 - accuracy: 0.7842 - val_loss: 0.3731 - val_accuracy: 0.8737
Epoch 112/150
3/3 [==============================] - 0s 107ms/step - loss: 0.4402 - accuracy: 0.8211 - val_loss: 0.4626 - val_accuracy: 0.7895
Epoch 138/150
3/3 [==============================] - 0s 101ms/step - loss: 0.4730 - accuracy: 0.8105 - val_loss: 0.3723 - val_accuracy: 0.8737
Epoch 113/150
3/3 [==============================] - 0s 86ms/step - loss: 0.4482 - accuracy: 0.8263 - val_loss: 0.4627 - val_accuracy: 0.7895
3/3 [==============================] - 0s 60ms/step - loss: 0.4769 - accuracy: 0.7947 - val_loss: 0.3719 - val_accuracy: 0.8842
Epoch 114/150
Epoch 139/150
3/3 [==============================] - 0s 76ms/step - loss: 0.4321 - accuracy: 0.8132 - val_loss: 0.4629 - val_accuracy: 0.7789
3/3 [==============================] - 0s 76ms/step - loss: 0.4637 - accuracy: 0.7789 - val_loss: 0.3711 - val_accuracy: 0.8842
Epoch 115/150
Epoch 140/150
3/3 [==============================] - 0s 68ms/step - loss: 0.4179 - accuracy: 0.8237 - val_loss: 0.4631 - val_accuracy: 0.7789
Epoch 141/150
3/3 [==============================] - 0s 73ms/step - loss: 0.4923 - accuracy: 0.7895 - val_loss: 0.3704 - val_accuracy: 0.8842
Epoch 116/150
3/3 [==============================] - 0s 50ms/step - loss: 0.4365 - accuracy: 0.8053 - val_loss: 0.4632 - val_accuracy: 0.7789
Epoch 142/150
3/3 [==============================] - 0s 80ms/step - loss: 0.4474 - accuracy: 0.7868 - val_loss: 0.3698 - val_accuracy: 0.8842
Epoch 117/150
3/3 [==============================] - 0s 62ms/step - loss: 0.4629 - accuracy: 0.8079 - val_loss: 0.4635 - val_accuracy: 0.7789
Epoch 143/150
3/3 [==============================] - 0s 70ms/step - loss: 0.4469 - accuracy: 0.7974 - val_loss: 0.3693 - val_accuracy: 0.8842
Epoch 118/150
3/3 [==============================] - 0s 69ms/step - loss: 0.4354 - accuracy: 0.8237 - val_loss: 0.4636 - val_accuracy: 0.7789
Epoch 144/150
3/3 [==============================] - 0s 71ms/step - loss: 0.4739 - accuracy: 0.7921 - val_loss: 0.3688 - val_accuracy: 0.8842
Epoch 119/150
3/3 [==============================] - 0s 76ms/step - loss: 0.4490 - accuracy: 0.8105 - val_loss: 0.4638 - val_accuracy: 0.7789
Epoch 145/150
3/3 [==============================] - 0s 84ms/step - loss: 0.5217 - accuracy: 0.7737 - val_loss: 0.3683 - val_accuracy: 0.8842
Epoch 120/150
3/3 [==============================] - 0s 88ms/step - loss: 0.4640 - accuracy: 0.7789 - val_loss: 0.4636 - val_accuracy: 0.7789
Epoch 146/150
3/3 [==============================] - 0s 91ms/step - loss: 0.4747 - accuracy: 0.8053 - val_loss: 0.3678 - val_accuracy: 0.8842
Epoch 121/150
3/3 [==============================] - 0s 83ms/step - loss: 0.4538 - accuracy: 0.8079 - val_loss: 0.4636 - val_accuracy: 0.7789
Epoch 147/150
3/3 [==============================] - 0s 74ms/step - loss: 0.4506 - accuracy: 0.8026 - val_loss: 0.3672 - val_accuracy: 0.8842
Epoch 122/150
3/3 [==============================] - 0s 82ms/step - loss: 0.4531 - accuracy: 0.8026 - val_loss: 0.4637 - val_accuracy: 0.7789
Epoch 148/150
3/3 [==============================] - 0s 74ms/step - loss: 0.4532 - accuracy: 0.8105 - val_loss: 0.3667 - val_accuracy: 0.8842
Epoch 123/150
3/3 [==============================] - 0s 83ms/step - loss: 0.4708 - accuracy: 0.8079 - val_loss: 0.4635 - val_accuracy: 0.7789
Epoch 149/150
3/3 [==============================] - 0s 92ms/step - loss: 0.4733 - accuracy: 0.7895 - val_loss: 0.3663 - val_accuracy: 0.8842
Epoch 124/150
3/3 [==============================] - 0s 106ms/step - loss: 0.4264 - accuracy: 0.8158 - val_loss: 0.4634 - val_accuracy: 0.7789
Epoch 150/150
3/3 [==============================] - 0s 79ms/step - loss: 0.4447 - accuracy: 0.8026 - val_loss: 0.3659 - val_accuracy: 0.8842
Epoch 125/150
3/3 [==============================] - 0s 75ms/step - loss: 0.4331 - accuracy: 0.8263 - val_loss: 0.4635 - val_accuracy: 0.7789
3/3 [==============================] - 0s 72ms/step - loss: 0.4276 - accuracy: 0.8211 - val_loss: 0.3656 - val_accuracy: 0.8842
Epoch 126/150
3/3 [==============================] - 0s 51ms/step - loss: 0.4713 - accuracy: 0.7974 - val_loss: 0.3651 - val_accuracy: 0.8842
Epoch 127/150
3/3 [==============================] - 0s 63ms/step - loss: 0.4339 - accuracy: 0.7895 - val_loss: 0.3645 - val_accuracy: 0.8842
Epoch 128/150
3/3 [==============================] - 0s 37ms/step - loss: 0.4580 - accuracy: 0.7763 - val_loss: 0.3638 - val_accuracy: 0.8842
Epoch 129/150
3/3 [==============================] - 0s 40ms/step - loss: 0.4800 - accuracy: 0.7737 - val_loss: 0.3635 - val_accuracy: 0.8842
Epoch 130/150
3/3 [==============================] - 0s 42ms/step - loss: 0.4938 - accuracy: 0.7868 - val_loss: 0.3630 - val_accuracy: 0.8842
Epoch 131/150
3/3 [==============================] - 0s 37ms/step - loss: 0.4646 - accuracy: 0.7974 - val_loss: 0.3623 - val_accuracy: 0.8842
Epoch 132/150
3/3 [==============================] - 0s 30ms/step - loss: 0.4675 - accuracy: 0.7816 - val_loss: 0.3617 - val_accuracy: 0.8842
Epoch 133/150
3/3 [==============================] - 0s 51ms/step - loss: 0.4704 - accuracy: 0.7842 - val_loss: 0.3612 - val_accuracy: 0.8842
Epoch 134/150
3/3 [==============================] - 0s 48ms/step - loss: 0.4509 - accuracy: 0.7947 - val_loss: 0.3609 - val_accuracy: 0.8842
Epoch 135/150
3/3 [==============================] - 0s 37ms/step - loss: 0.4543 - accuracy: 0.8079 - val_loss: 0.3604 - val_accuracy: 0.8842
Epoch 136/150
3/3 [==============================] - 0s 43ms/step - loss: 0.4956 - accuracy: 0.7789 - val_loss: 0.3599 - val_accuracy: 0.8842
Epoch 137/150
3/3 [==============================] - 0s 46ms/step - loss: 0.5031 - accuracy: 0.7868 - val_loss: 0.3595 - val_accuracy: 0.8842
Epoch 138/150
3/3 [==============================] - 0s 43ms/step - loss: 0.4985 - accuracy: 0.7816 - val_loss: 0.3591 - val_accuracy: 0.8842
Epoch 139/150
3/3 [==============================] - 0s 44ms/step - loss: 0.5045 - accuracy: 0.7842 - val_loss: 0.3587 - val_accuracy: 0.8737
Epoch 140/150
3/3 [==============================] - 0s 38ms/step - loss: 0.4496 - accuracy: 0.8132 - val_loss: 0.3583 - val_accuracy: 0.8737
Epoch 141/150
3/3 [==============================] - 0s 48ms/step - loss: 0.4909 - accuracy: 0.7763 - val_loss: 0.3579 - val_accuracy: 0.8737
Epoch 142/150
3/3 [==============================] - 0s 47ms/step - loss: 0.4471 - accuracy: 0.8132 - val_loss: 0.3577 - val_accuracy: 0.8737
Epoch 143/150
3/3 [==============================] - 0s 36ms/step - loss: 0.4822 - accuracy: 0.8000 - val_loss: 0.3572 - val_accuracy: 0.8737
Epoch 144/150
3/3 [==============================] - 0s 39ms/step - loss: 0.4754 - accuracy: 0.7711 - val_loss: 0.3570 - val_accuracy: 0.8737
Epoch 145/150
3/3 [==============================] - 0s 37ms/step - loss: 0.4515 - accuracy: 0.8053 - val_loss: 0.3568 - val_accuracy: 0.8737
Epoch 146/150
3/3 [==============================] - 0s 33ms/step - loss: 0.4754 - accuracy: 0.7868 - val_loss: 0.3564 - val_accuracy: 0.8737
Epoch 147/150
3/3 [==============================] - 0s 33ms/step - loss: 0.4615 - accuracy: 0.7921 - val_loss: 0.3560 - val_accuracy: 0.8737
Epoch 148/150
3/3 [==============================] - 0s 41ms/step - loss: 0.4378 - accuracy: 0.8026 - val_loss: 0.3556 - val_accuracy: 0.8737
Epoch 149/150
3/3 [==============================] - 0s 41ms/step - loss: 0.5133 - accuracy: 0.7658 - val_loss: 0.3555 - val_accuracy: 0.8737
Epoch 150/150
3/3 [==============================] - 0s 36ms/step - loss: 0.4748 - accuracy: 0.7763 - val_loss: 0.3552 - val_accuracy: 0.8737
2/2 [==============================] - 0s 16ms/step - loss: 0.4212 - accuracy: 0.8228
Epoch 1/150
3/3 [==============================] - 2s 239ms/step - loss: 0.8223 - accuracy: 0.5356 - val_loss: 0.6670 - val_accuracy: 0.6421
Epoch 2/150
3/3 [==============================] - 0s 42ms/step - loss: 0.7731 - accuracy: 0.5435 - val_loss: 0.6532 - val_accuracy: 0.6526
Epoch 3/150
3/3 [==============================] - 0s 40ms/step - loss: 0.7490 - accuracy: 0.5963 - val_loss: 0.6418 - val_accuracy: 0.6632
Epoch 4/150
3/3 [==============================] - 0s 39ms/step - loss: 0.7250 - accuracy: 0.6306 - val_loss: 0.6324 - val_accuracy: 0.6526
Epoch 5/150
3/3 [==============================] - 0s 36ms/step - loss: 0.6784 - accuracy: 0.6332 - val_loss: 0.6237 - val_accuracy: 0.6526
Epoch 6/150
3/3 [==============================] - 0s 37ms/step - loss: 0.6455 - accuracy: 0.6834 - val_loss: 0.6157 - val_accuracy: 0.6632
Epoch 7/150
3/3 [==============================] - 0s 45ms/step - loss: 0.5933 - accuracy: 0.6992 - val_loss: 0.6083 - val_accuracy: 0.7053
Epoch 8/150
3/3 [==============================] - 0s 46ms/step - loss: 0.6366 - accuracy: 0.6675 - val_loss: 0.6014 - val_accuracy: 0.7053
Epoch 9/150
3/3 [==============================] - 0s 44ms/step - loss: 0.6127 - accuracy: 0.6939 - val_loss: 0.5952 - val_accuracy: 0.7053
Epoch 10/150
3/3 [==============================] - 0s 41ms/step - loss: 0.5907 - accuracy: 0.7098 - val_loss: 0.5898 - val_accuracy: 0.6842
Epoch 11/150
3/3 [==============================] - 0s 43ms/step - loss: 0.5871 - accuracy: 0.7071 - val_loss: 0.5843 - val_accuracy: 0.6842
Epoch 12/150
3/3 [==============================] - 0s 40ms/step - loss: 0.5410 - accuracy: 0.7361 - val_loss: 0.5788 - val_accuracy: 0.6842
Epoch 13/150
3/3 [==============================] - 0s 39ms/step - loss: 0.5368 - accuracy: 0.7282 - val_loss: 0.5740 - val_accuracy: 0.6947
Epoch 14/150
3/3 [==============================] - 0s 49ms/step - loss: 0.5118 - accuracy: 0.7467 - val_loss: 0.5697 - val_accuracy: 0.6947
Epoch 15/150
3/3 [==============================] - 0s 39ms/step - loss: 0.5228 - accuracy: 0.7361 - val_loss: 0.5654 - val_accuracy: 0.6947
Epoch 16/150
3/3 [==============================] - 0s 40ms/step - loss: 0.5409 - accuracy: 0.7335 - val_loss: 0.5607 - val_accuracy: 0.6947
Epoch 17/150
3/3 [==============================] - 0s 38ms/step - loss: 0.5666 - accuracy: 0.7256 - val_loss: 0.5563 - val_accuracy: 0.7053
Epoch 18/150
3/3 [==============================] - 0s 39ms/step - loss: 0.5319 - accuracy: 0.7493 - val_loss: 0.5520 - val_accuracy: 0.7158
Epoch 19/150
3/3 [==============================] - 0s 44ms/step - loss: 0.5042 - accuracy: 0.7599 - val_loss: 0.5481 - val_accuracy: 0.7158
Epoch 20/150
3/3 [==============================] - 0s 37ms/step - loss: 0.4800 - accuracy: 0.7731 - val_loss: 0.5443 - val_accuracy: 0.7368
Epoch 21/150
3/3 [==============================] - 0s 44ms/step - loss: 0.4645 - accuracy: 0.7916 - val_loss: 0.5407 - val_accuracy: 0.7474
Epoch 22/150
3/3 [==============================] - 0s 40ms/step - loss: 0.4721 - accuracy: 0.7731 - val_loss: 0.5370 - val_accuracy: 0.7474
Epoch 23/150
3/3 [==============================] - 0s 48ms/step - loss: 0.4697 - accuracy: 0.7810 - val_loss: 0.5333 - val_accuracy: 0.7579
Epoch 24/150
3/3 [==============================] - 0s 44ms/step - loss: 0.4710 - accuracy: 0.7916 - val_loss: 0.5300 - val_accuracy: 0.7579
Epoch 25/150
3/3 [==============================] - 0s 53ms/step - loss: 0.4363 - accuracy: 0.7836 - val_loss: 0.5266 - val_accuracy: 0.7579
Epoch 26/150
3/3 [==============================] - 0s 50ms/step - loss: 0.4581 - accuracy: 0.7863 - val_loss: 0.5234 - val_accuracy: 0.7684
Epoch 27/150
3/3 [==============================] - 0s 57ms/step - loss: 0.4878 - accuracy: 0.7863 - val_loss: 0.5206 - val_accuracy: 0.7684
Epoch 28/150
3/3 [==============================] - 0s 52ms/step - loss: 0.4553 - accuracy: 0.7836 - val_loss: 0.5177 - val_accuracy: 0.7684
Epoch 29/150
3/3 [==============================] - 0s 55ms/step - loss: 0.4489 - accuracy: 0.8074 - val_loss: 0.5153 - val_accuracy: 0.7579
Epoch 30/150
3/3 [==============================] - 0s 49ms/step - loss: 0.4852 - accuracy: 0.7731 - val_loss: 0.5130 - val_accuracy: 0.7684
Epoch 31/150
3/3 [==============================] - 0s 56ms/step - loss: 0.4660 - accuracy: 0.7704 - val_loss: 0.5108 - val_accuracy: 0.7684
Epoch 32/150
3/3 [==============================] - 0s 53ms/step - loss: 0.4463 - accuracy: 0.7810 - val_loss: 0.5083 - val_accuracy: 0.7789
Epoch 33/150
3/3 [==============================] - 0s 52ms/step - loss: 0.4513 - accuracy: 0.7889 - val_loss: 0.5060 - val_accuracy: 0.7895
Epoch 34/150
3/3 [==============================] - 0s 46ms/step - loss: 0.4615 - accuracy: 0.7968 - val_loss: 0.5038 - val_accuracy: 0.8000
Epoch 35/150
3/3 [==============================] - 0s 54ms/step - loss: 0.4646 - accuracy: 0.7942 - val_loss: 0.5015 - val_accuracy: 0.8000
Epoch 36/150
3/3 [==============================] - 0s 62ms/step - loss: 0.4111 - accuracy: 0.8311 - val_loss: 0.4995 - val_accuracy: 0.8000
Epoch 37/150
3/3 [==============================] - 0s 45ms/step - loss: 0.4371 - accuracy: 0.8179 - val_loss: 0.4973 - val_accuracy: 0.8000
Epoch 38/150
3/3 [==============================] - 0s 53ms/step - loss: 0.4823 - accuracy: 0.7942 - val_loss: 0.4952 - val_accuracy: 0.7895
Epoch 39/150
3/3 [==============================] - 0s 64ms/step - loss: 0.4582 - accuracy: 0.8153 - val_loss: 0.4933 - val_accuracy: 0.7895
Epoch 40/150
3/3 [==============================] - 0s 59ms/step - loss: 0.4640 - accuracy: 0.7889 - val_loss: 0.4914 - val_accuracy: 0.7895
Epoch 41/150
3/3 [==============================] - 0s 59ms/step - loss: 0.4312 - accuracy: 0.8179 - val_loss: 0.4901 - val_accuracy: 0.7895
Epoch 42/150
3/3 [==============================] - 0s 59ms/step - loss: 0.4420 - accuracy: 0.7942 - val_loss: 0.4884 - val_accuracy: 0.8000
Epoch 43/150
3/3 [==============================] - 0s 58ms/step - loss: 0.4258 - accuracy: 0.8179 - val_loss: 0.4866 - val_accuracy: 0.8000
Epoch 44/150
3/3 [==============================] - 0s 55ms/step - loss: 0.4445 - accuracy: 0.8127 - val_loss: 0.4852 - val_accuracy: 0.8105
Epoch 45/150
3/3 [==============================] - 0s 52ms/step - loss: 0.4380 - accuracy: 0.8259 - val_loss: 0.4836 - val_accuracy: 0.8105
Epoch 46/150
3/3 [==============================] - 0s 56ms/step - loss: 0.4544 - accuracy: 0.7968 - val_loss: 0.4822 - val_accuracy: 0.8105
Epoch 47/150
3/3 [==============================] - 0s 52ms/step - loss: 0.3926 - accuracy: 0.8179 - val_loss: 0.4809 - val_accuracy: 0.8105
Epoch 48/150
3/3 [==============================] - 0s 55ms/step - loss: 0.4260 - accuracy: 0.8074 - val_loss: 0.4796 - val_accuracy: 0.8105
Epoch 49/150
3/3 [==============================] - 0s 55ms/step - loss: 0.4058 - accuracy: 0.8338 - val_loss: 0.4782 - val_accuracy: 0.8105
Epoch 50/150
3/3 [==============================] - 0s 53ms/step - loss: 0.4271 - accuracy: 0.8127 - val_loss: 0.4768 - val_accuracy: 0.8105
Epoch 51/150
3/3 [==============================] - 0s 44ms/step - loss: 0.4409 - accuracy: 0.8100 - val_loss: 0.4754 - val_accuracy: 0.8211
Epoch 52/150
3/3 [==============================] - 0s 49ms/step - loss: 0.4049 - accuracy: 0.8153 - val_loss: 0.4741 - val_accuracy: 0.8211
Epoch 53/150
3/3 [==============================] - 0s 44ms/step - loss: 0.4108 - accuracy: 0.8232 - val_loss: 0.4729 - val_accuracy: 0.8211
Epoch 54/150
3/3 [==============================] - 0s 54ms/step - loss: 0.4018 - accuracy: 0.8259 - val_loss: 0.4716 - val_accuracy: 0.8105
Epoch 55/150
3/3 [==============================] - 0s 54ms/step - loss: 0.4582 - accuracy: 0.8047 - val_loss: 0.4707 - val_accuracy: 0.8105
Epoch 56/150
3/3 [==============================] - 0s 51ms/step - loss: 0.3807 - accuracy: 0.8311 - val_loss: 0.4699 - val_accuracy: 0.8105
Epoch 57/150
3/3 [==============================] - 0s 50ms/step - loss: 0.3916 - accuracy: 0.8259 - val_loss: 0.4690 - val_accuracy: 0.8105
Epoch 58/150
3/3 [==============================] - 0s 54ms/step - loss: 0.4269 - accuracy: 0.8074 - val_loss: 0.4680 - val_accuracy: 0.8105
Epoch 59/150
3/3 [==============================] - 0s 51ms/step - loss: 0.4141 - accuracy: 0.8285 - val_loss: 0.4674 - val_accuracy: 0.8105
Epoch 60/150
3/3 [==============================] - 0s 56ms/step - loss: 0.4121 - accuracy: 0.8100 - val_loss: 0.4670 - val_accuracy: 0.8105
Epoch 61/150
3/3 [==============================] - 0s 48ms/step - loss: 0.4216 - accuracy: 0.8127 - val_loss: 0.4665 - val_accuracy: 0.8105
Epoch 62/150
3/3 [==============================] - 0s 61ms/step - loss: 0.3917 - accuracy: 0.8259 - val_loss: 0.4657 - val_accuracy: 0.8105
Epoch 63/150
3/3 [==============================] - 0s 74ms/step - loss: 0.3944 - accuracy: 0.8549 - val_loss: 0.4649 - val_accuracy: 0.8105
Epoch 64/150
3/3 [==============================] - 0s 71ms/step - loss: 0.4036 - accuracy: 0.8259 - val_loss: 0.4643 - val_accuracy: 0.8105
Epoch 65/150
3/3 [==============================] - 0s 73ms/step - loss: 0.3969 - accuracy: 0.8391 - val_loss: 0.4638 - val_accuracy: 0.8105
Epoch 66/150
2/2 [==============================] - 0s 16ms/step - loss: 0.3857 - accuracy: 0.8354
3/3 [==============================] - 0s 77ms/step - loss: 0.3823 - accuracy: 0.8259 - val_loss: 0.4633 - val_accuracy: 0.8105
Epoch 67/150
3/3 [==============================] - 0s 83ms/step - loss: 0.3854 - accuracy: 0.8391 - val_loss: 0.4629 - val_accuracy: 0.8105
Epoch 68/150
3/3 [==============================] - 0s 84ms/step - loss: 0.4371 - accuracy: 0.8127 - val_loss: 0.4626 - val_accuracy: 0.8105
Epoch 69/150
3/3 [==============================] - 0s 78ms/step - loss: 0.4030 - accuracy: 0.8259 - val_loss: 0.4622 - val_accuracy: 0.8105
Epoch 70/150
1/3 [=========>....................] - ETA: 0s - loss: 0.3719 - accuracy: 0.8281Epoch 1/150
3/3 [==============================] - 0s 118ms/step - loss: 0.3910 - accuracy: 0.8153 - val_loss: 0.4621 - val_accuracy: 0.8000
Epoch 71/150
3/3 [==============================] - 0s 88ms/step - loss: 0.4342 - accuracy: 0.8259 - val_loss: 0.4619 - val_accuracy: 0.8000
Epoch 72/150
3/3 [==============================] - 0s 109ms/step - loss: 0.3935 - accuracy: 0.8259 - val_loss: 0.4616 - val_accuracy: 0.8000
Epoch 73/150
3/3 [==============================] - 0s 69ms/step - loss: 0.3602 - accuracy: 0.8443 - val_loss: 0.4613 - val_accuracy: 0.8000
Epoch 74/150
3/3 [==============================] - 0s 73ms/step - loss: 0.4014 - accuracy: 0.8417 - val_loss: 0.4611 - val_accuracy: 0.8000
Epoch 75/150
3/3 [==============================] - 0s 46ms/step - loss: 0.3574 - accuracy: 0.8364 - val_loss: 0.4609 - val_accuracy: 0.8000
Epoch 76/150
3/3 [==============================] - 0s 73ms/step - loss: 0.4002 - accuracy: 0.8074 - val_loss: 0.4610 - val_accuracy: 0.8000
Epoch 77/150
3/3 [==============================] - 0s 70ms/step - loss: 0.3958 - accuracy: 0.8259 - val_loss: 0.4609 - val_accuracy: 0.8000
Epoch 78/150
3/3 [==============================] - 0s 66ms/step - loss: 0.3846 - accuracy: 0.8417 - val_loss: 0.4611 - val_accuracy: 0.8000
Epoch 79/150
3/3 [==============================] - 0s 74ms/step - loss: 0.3779 - accuracy: 0.8338 - val_loss: 0.4615 - val_accuracy: 0.8000
Epoch 80/150
3/3 [==============================] - 0s 76ms/step - loss: 0.3986 - accuracy: 0.8259 - val_loss: 0.4619 - val_accuracy: 0.8000
Epoch 81/150
3/3 [==============================] - 0s 95ms/step - loss: 0.3816 - accuracy: 0.8522 - val_loss: 0.4623 - val_accuracy: 0.8000
Epoch 82/150
3/3 [==============================] - 0s 88ms/step - loss: 0.3833 - accuracy: 0.8364 - val_loss: 0.4628 - val_accuracy: 0.8000
Epoch 83/150
3/3 [==============================] - 0s 69ms/step - loss: 0.4038 - accuracy: 0.8364 - val_loss: 0.4630 - val_accuracy: 0.8000
Epoch 84/150
3/3 [==============================] - 0s 72ms/step - loss: 0.3882 - accuracy: 0.8232 - val_loss: 0.4633 - val_accuracy: 0.8000
Epoch 85/150
3/3 [==============================] - 0s 64ms/step - loss: 0.3619 - accuracy: 0.8443 - val_loss: 0.4634 - val_accuracy: 0.8000
Epoch 86/150
3/3 [==============================] - 0s 61ms/step - loss: 0.4075 - accuracy: 0.8311 - val_loss: 0.4635 - val_accuracy: 0.8000
Epoch 87/150
3/3 [==============================] - 0s 60ms/step - loss: 0.3942 - accuracy: 0.8338 - val_loss: 0.4633 - val_accuracy: 0.8000
Epoch 88/150
3/3 [==============================] - 0s 64ms/step - loss: 0.3910 - accuracy: 0.8311 - val_loss: 0.4636 - val_accuracy: 0.8000
Epoch 89/150
3/3 [==============================] - 0s 62ms/step - loss: 0.3809 - accuracy: 0.8470 - val_loss: 0.4639 - val_accuracy: 0.8000
Epoch 90/150
3/3 [==============================] - 0s 59ms/step - loss: 0.3680 - accuracy: 0.8470 - val_loss: 0.4639 - val_accuracy: 0.8000
Epoch 91/150
3/3 [==============================] - 0s 65ms/step - loss: 0.3694 - accuracy: 0.8522 - val_loss: 0.4639 - val_accuracy: 0.8000
Epoch 92/150
3/3 [==============================] - 0s 64ms/step - loss: 0.3924 - accuracy: 0.8602 - val_loss: 0.4637 - val_accuracy: 0.8000
Epoch 93/150
3/3 [==============================] - 0s 65ms/step - loss: 0.3608 - accuracy: 0.8522 - val_loss: 0.4638 - val_accuracy: 0.8000
Epoch 94/150
3/3 [==============================] - 0s 68ms/step - loss: 0.3650 - accuracy: 0.8496 - val_loss: 0.4640 - val_accuracy: 0.8000
Epoch 95/150
3/3 [==============================] - 0s 59ms/step - loss: 0.3784 - accuracy: 0.8707 - val_loss: 0.4642 - val_accuracy: 0.8000
Epoch 96/150
3/3 [==============================] - 0s 70ms/step - loss: 0.3874 - accuracy: 0.8470 - val_loss: 0.4643 - val_accuracy: 0.8000
Epoch 97/150
3/3 [==============================] - 0s 58ms/step - loss: 0.3720 - accuracy: 0.8628 - val_loss: 0.4648 - val_accuracy: 0.8000
Epoch 98/150
3/3 [==============================] - 0s 58ms/step - loss: 0.3763 - accuracy: 0.8496 - val_loss: 0.4653 - val_accuracy: 0.8000
Epoch 99/150
3/3 [==============================] - 0s 65ms/step - loss: 0.4071 - accuracy: 0.8232 - val_loss: 0.4657 - val_accuracy: 0.8000
Epoch 100/150
3/3 [==============================] - 0s 65ms/step - loss: 0.3885 - accuracy: 0.8443 - val_loss: 0.4663 - val_accuracy: 0.8000
Epoch 101/150
3/3 [==============================] - 0s 56ms/step - loss: 0.3987 - accuracy: 0.8417 - val_loss: 0.4666 - val_accuracy: 0.8000
Epoch 102/150
3/3 [==============================] - 0s 62ms/step - loss: 0.3814 - accuracy: 0.8417 - val_loss: 0.4670 - val_accuracy: 0.8000
Epoch 103/150
3/3 [==============================] - 0s 60ms/step - loss: 0.3905 - accuracy: 0.8311 - val_loss: 0.4675 - val_accuracy: 0.8000
Epoch 104/150
3/3 [==============================] - 0s 54ms/step - loss: 0.3847 - accuracy: 0.8549 - val_loss: 0.4682 - val_accuracy: 0.8000
Epoch 105/150
3/3 [==============================] - 0s 61ms/step - loss: 0.3801 - accuracy: 0.8628 - val_loss: 0.4691 - val_accuracy: 0.8000
Epoch 106/150
3/3 [==============================] - 0s 62ms/step - loss: 0.3680 - accuracy: 0.8496 - val_loss: 0.4700 - val_accuracy: 0.8000
Epoch 107/150
3/3 [==============================] - 6s 521ms/step - loss: 0.8634 - accuracy: 0.5289 - val_loss: 0.6596 - val_accuracy: 0.6000
Epoch 2/150
3/3 [==============================] - 0s 67ms/step - loss: 0.3786 - accuracy: 0.8417 - val_loss: 0.4707 - val_accuracy: 0.8000
Epoch 108/150
3/3 [==============================] - 0s 69ms/step - loss: 0.7335 - accuracy: 0.6211 - val_loss: 0.6461 - val_accuracy: 0.6421
Epoch 3/150
3/3 [==============================] - 0s 68ms/step - loss: 0.3666 - accuracy: 0.8522 - val_loss: 0.4714 - val_accuracy: 0.8000
Epoch 109/150
3/3 [==============================] - 0s 63ms/step - loss: 0.7632 - accuracy: 0.5763 - val_loss: 0.6346 - val_accuracy: 0.6421
Epoch 4/150
3/3 [==============================] - 0s 73ms/step - loss: 0.3849 - accuracy: 0.8417 - val_loss: 0.4716 - val_accuracy: 0.8000
Epoch 110/150
3/3 [==============================] - 0s 56ms/step - loss: 0.6889 - accuracy: 0.6263 - val_loss: 0.6244 - val_accuracy: 0.6316
Epoch 5/150
3/3 [==============================] - 0s 56ms/step - loss: 0.6865 - accuracy: 0.6447 - val_loss: 0.6160 - val_accuracy: 0.6316
3/3 [==============================] - 0s 67ms/step - loss: 0.3865 - accuracy: 0.8364 - val_loss: 0.4717 - val_accuracy: 0.8000
Epoch 111/150
Epoch 6/150
3/3 [==============================] - 0s 49ms/step - loss: 0.3845 - accuracy: 0.8549 - val_loss: 0.4723 - val_accuracy: 0.8000
Epoch 112/150
3/3 [==============================] - 0s 88ms/step - loss: 0.6906 - accuracy: 0.6421 - val_loss: 0.6082 - val_accuracy: 0.6211
Epoch 7/150
3/3 [==============================] - 0s 62ms/step - loss: 0.3700 - accuracy: 0.8391 - val_loss: 0.4726 - val_accuracy: 0.8000
Epoch 113/150
3/3 [==============================] - 0s 67ms/step - loss: 0.6068 - accuracy: 0.7079 - val_loss: 0.6012 - val_accuracy: 0.6211
Epoch 8/150
3/3 [==============================] - 0s 72ms/step - loss: 0.3508 - accuracy: 0.8602 - val_loss: 0.4730 - val_accuracy: 0.8000
Epoch 114/150
3/3 [==============================] - 0s 81ms/step - loss: 0.6104 - accuracy: 0.6895 - val_loss: 0.5948 - val_accuracy: 0.6421
Epoch 9/150
3/3 [==============================] - 0s 57ms/step - loss: 0.3646 - accuracy: 0.8443 - val_loss: 0.4733 - val_accuracy: 0.8000
Epoch 115/150
3/3 [==============================] - 0s 69ms/step - loss: 0.5774 - accuracy: 0.6921 - val_loss: 0.5889 - val_accuracy: 0.6632
Epoch 10/150
3/3 [==============================] - 0s 67ms/step - loss: 0.3771 - accuracy: 0.8470 - val_loss: 0.4737 - val_accuracy: 0.8000
Epoch 116/150
3/3 [==============================] - 0s 74ms/step - loss: 0.6129 - accuracy: 0.7053 - val_loss: 0.5837 - val_accuracy: 0.6737
Epoch 11/150
3/3 [==============================] - 0s 64ms/step - loss: 0.3175 - accuracy: 0.8760 - val_loss: 0.4741 - val_accuracy: 0.8000
Epoch 117/150
3/3 [==============================] - 0s 59ms/step - loss: 0.5508 - accuracy: 0.7368 - val_loss: 0.5788 - val_accuracy: 0.6737
Epoch 12/150
3/3 [==============================] - 0s 59ms/step - loss: 0.3548 - accuracy: 0.8549 - val_loss: 0.4744 - val_accuracy: 0.8000
Epoch 118/150
3/3 [==============================] - 0s 62ms/step - loss: 0.5664 - accuracy: 0.7105 - val_loss: 0.5739 - val_accuracy: 0.6842
Epoch 13/150
3/3 [==============================] - 0s 71ms/step - loss: 0.3721 - accuracy: 0.8522 - val_loss: 0.4745 - val_accuracy: 0.8000
Epoch 119/150
3/3 [==============================] - 0s 65ms/step - loss: 0.5770 - accuracy: 0.7474 - val_loss: 0.5690 - val_accuracy: 0.7053
Epoch 14/150
3/3 [==============================] - 0s 57ms/step - loss: 0.3890 - accuracy: 0.8470 - val_loss: 0.4748 - val_accuracy: 0.8000
Epoch 120/150
3/3 [==============================] - 0s 56ms/step - loss: 0.3577 - accuracy: 0.8443 - val_loss: 0.4750 - val_accuracy: 0.8105
Epoch 121/150
3/3 [==============================] - 0s 58ms/step - loss: 0.6026 - accuracy: 0.7316 - val_loss: 0.5644 - val_accuracy: 0.7053
Epoch 15/150
3/3 [==============================] - 0s 66ms/step - loss: 0.3744 - accuracy: 0.8602 - val_loss: 0.4752 - val_accuracy: 0.8105
3/3 [==============================] - 0s 67ms/step - loss: 0.5823 - accuracy: 0.7368 - val_loss: 0.5602 - val_accuracy: 0.7053
Epoch 122/150
Epoch 16/150
3/3 [==============================] - 0s 48ms/step - loss: 0.3901 - accuracy: 0.8417 - val_loss: 0.4752 - val_accuracy: 0.8105
Epoch 123/150
3/3 [==============================] - 0s 63ms/step - loss: 0.5710 - accuracy: 0.7526 - val_loss: 0.5561 - val_accuracy: 0.7053
Epoch 17/150
3/3 [==============================] - 0s 65ms/step - loss: 0.3532 - accuracy: 0.8628 - val_loss: 0.4753 - val_accuracy: 0.8105
Epoch 124/150
3/3 [==============================] - 0s 86ms/step - loss: 0.5755 - accuracy: 0.7263 - val_loss: 0.5517 - val_accuracy: 0.7263
Epoch 18/150
3/3 [==============================] - 0s 40ms/step - loss: 0.3607 - accuracy: 0.8443 - val_loss: 0.4759 - val_accuracy: 0.8105
Epoch 125/150
3/3 [==============================] - 0s 53ms/step - loss: 0.3780 - accuracy: 0.8470 - val_loss: 0.4761 - val_accuracy: 0.8105
Epoch 126/150
3/3 [==============================] - 0s 68ms/step - loss: 0.5352 - accuracy: 0.7553 - val_loss: 0.5479 - val_accuracy: 0.7368
Epoch 19/150
3/3 [==============================] - 0s 69ms/step - loss: 0.3544 - accuracy: 0.8391 - val_loss: 0.4763 - val_accuracy: 0.8105
Epoch 127/150
3/3 [==============================] - 0s 74ms/step - loss: 0.5246 - accuracy: 0.7737 - val_loss: 0.5442 - val_accuracy: 0.7368
Epoch 20/150
3/3 [==============================] - 0s 53ms/step - loss: 0.5276 - accuracy: 0.7579 - val_loss: 0.5405 - val_accuracy: 0.7368
Epoch 21/150
3/3 [==============================] - 0s 70ms/step - loss: 0.3401 - accuracy: 0.8575 - val_loss: 0.4769 - val_accuracy: 0.8105
Epoch 128/150
3/3 [==============================] - 0s 66ms/step - loss: 0.3829 - accuracy: 0.8628 - val_loss: 0.4768 - val_accuracy: 0.8105
3/3 [==============================] - 0s 67ms/step - loss: 0.5212 - accuracy: 0.7553 - val_loss: 0.5368 - val_accuracy: 0.7474
Epoch 129/150
1/3 [=========>....................] - ETA: 0s - loss: 0.3625 - accuracy: 0.8438Epoch 22/150
3/3 [==============================] - 0s 69ms/step - loss: 0.3735 - accuracy: 0.8522 - val_loss: 0.4771 - val_accuracy: 0.8105
Epoch 130/150
3/3 [==============================] - 0s 78ms/step - loss: 0.5602 - accuracy: 0.7316 - val_loss: 0.5334 - val_accuracy: 0.7684
Epoch 23/150
3/3 [==============================] - 0s 61ms/step - loss: 0.3528 - accuracy: 0.8364 - val_loss: 0.4776 - val_accuracy: 0.8105
Epoch 131/150
3/3 [==============================] - 0s 88ms/step - loss: 0.5134 - accuracy: 0.7763 - val_loss: 0.5303 - val_accuracy: 0.7684
Epoch 24/150
3/3 [==============================] - 0s 73ms/step - loss: 0.3594 - accuracy: 0.8681 - val_loss: 0.4780 - val_accuracy: 0.8105
Epoch 132/150
3/3 [==============================] - 0s 71ms/step - loss: 0.4804 - accuracy: 0.8026 - val_loss: 0.5271 - val_accuracy: 0.7684
Epoch 25/150
3/3 [==============================] - 0s 68ms/step - loss: 0.3601 - accuracy: 0.8285 - val_loss: 0.4782 - val_accuracy: 0.8105
Epoch 133/150
3/3 [==============================] - 0s 69ms/step - loss: 0.4659 - accuracy: 0.7632 - val_loss: 0.5242 - val_accuracy: 0.7684
Epoch 26/150
3/3 [==============================] - 0s 72ms/step - loss: 0.3552 - accuracy: 0.8549 - val_loss: 0.4786 - val_accuracy: 0.8105
Epoch 134/150
3/3 [==============================] - 0s 66ms/step - loss: 0.5705 - accuracy: 0.7447 - val_loss: 0.5211 - val_accuracy: 0.7684
Epoch 27/150
3/3 [==============================] - 0s 67ms/step - loss: 0.3521 - accuracy: 0.8602 - val_loss: 0.4789 - val_accuracy: 0.8105
Epoch 135/150
3/3 [==============================] - 0s 71ms/step - loss: 0.5272 - accuracy: 0.7474 - val_loss: 0.5181 - val_accuracy: 0.7789
Epoch 28/150
3/3 [==============================] - 0s 72ms/step - loss: 0.3725 - accuracy: 0.8391 - val_loss: 0.4792 - val_accuracy: 0.8105
Epoch 136/150
3/3 [==============================] - 0s 69ms/step - loss: 0.4866 - accuracy: 0.7711 - val_loss: 0.5151 - val_accuracy: 0.7789
Epoch 29/150
3/3 [==============================] - 0s 66ms/step - loss: 0.3738 - accuracy: 0.8417 - val_loss: 0.4792 - val_accuracy: 0.8105
Epoch 137/150
3/3 [==============================] - 0s 69ms/step - loss: 0.5016 - accuracy: 0.7842 - val_loss: 0.5124 - val_accuracy: 0.7789
Epoch 30/150
3/3 [==============================] - 0s 66ms/step - loss: 0.3393 - accuracy: 0.8654 - val_loss: 0.4794 - val_accuracy: 0.8105
Epoch 138/150
3/3 [==============================] - 0s 72ms/step - loss: 0.4835 - accuracy: 0.7737 - val_loss: 0.5098 - val_accuracy: 0.7895
Epoch 31/150
3/3 [==============================] - 0s 62ms/step - loss: 0.3674 - accuracy: 0.8522 - val_loss: 0.4797 - val_accuracy: 0.8000
Epoch 139/150
3/3 [==============================] - 0s 85ms/step - loss: 0.5245 - accuracy: 0.7684 - val_loss: 0.5073 - val_accuracy: 0.7895
Epoch 32/150
3/3 [==============================] - 0s 90ms/step - loss: 0.3620 - accuracy: 0.8549 - val_loss: 0.4798 - val_accuracy: 0.8000
Epoch 140/150
3/3 [==============================] - 0s 87ms/step - loss: 0.4961 - accuracy: 0.7684 - val_loss: 0.5049 - val_accuracy: 0.7895
Epoch 33/150
3/3 [==============================] - 0s 75ms/step - loss: 0.3745 - accuracy: 0.8470 - val_loss: 0.4805 - val_accuracy: 0.8000
Epoch 141/150
3/3 [==============================] - 0s 99ms/step - loss: 0.4983 - accuracy: 0.7684 - val_loss: 0.5024 - val_accuracy: 0.7895
Epoch 34/150
3/3 [==============================] - 0s 98ms/step - loss: 0.3580 - accuracy: 0.8575 - val_loss: 0.4812 - val_accuracy: 0.8105
Epoch 142/150
3/3 [==============================] - 0s 91ms/step - loss: 0.4934 - accuracy: 0.7895 - val_loss: 0.5001 - val_accuracy: 0.7895
3/3 [==============================] - 0s 76ms/step - loss: 0.3492 - accuracy: 0.8417 - val_loss: 0.4817 - val_accuracy: 0.8105
Epoch 35/150
Epoch 143/150
3/3 [==============================] - 0s 87ms/step - loss: 0.3472 - accuracy: 0.8654 - val_loss: 0.4820 - val_accuracy: 0.8105
3/3 [==============================] - 0s 95ms/step - loss: 0.4715 - accuracy: 0.7842 - val_loss: 0.4979 - val_accuracy: 0.7895
Epoch 36/150
Epoch 144/150
3/3 [==============================] - 0s 106ms/step - loss: 0.3360 - accuracy: 0.8549 - val_loss: 0.4822 - val_accuracy: 0.8105
Epoch 145/150
3/3 [==============================] - 0s 115ms/step - loss: 0.4773 - accuracy: 0.7947 - val_loss: 0.4959 - val_accuracy: 0.7895
Epoch 37/150
3/3 [==============================] - 0s 79ms/step - loss: 0.3539 - accuracy: 0.8496 - val_loss: 0.4824 - val_accuracy: 0.8105
Epoch 146/150
3/3 [==============================] - 0s 91ms/step - loss: 0.5200 - accuracy: 0.7868 - val_loss: 0.4940 - val_accuracy: 0.7895
Epoch 38/150
3/3 [==============================] - 0s 91ms/step - loss: 0.3875 - accuracy: 0.8417 - val_loss: 0.4832 - val_accuracy: 0.8105
Epoch 147/150
3/3 [==============================] - 0s 98ms/step - loss: 0.4641 - accuracy: 0.7974 - val_loss: 0.4921 - val_accuracy: 0.7895
Epoch 39/150
3/3 [==============================] - 0s 85ms/step - loss: 0.3428 - accuracy: 0.8522 - val_loss: 0.4842 - val_accuracy: 0.8105
Epoch 148/150
3/3 [==============================] - 0s 92ms/step - loss: 0.5023 - accuracy: 0.7632 - val_loss: 0.4904 - val_accuracy: 0.7895
Epoch 40/150
3/3 [==============================] - 0s 85ms/step - loss: 0.3818 - accuracy: 0.8311 - val_loss: 0.4849 - val_accuracy: 0.8105
Epoch 149/150
3/3 [==============================] - 0s 88ms/step - loss: 0.5287 - accuracy: 0.7658 - val_loss: 0.4885 - val_accuracy: 0.7895
Epoch 41/150
3/3 [==============================] - 0s 89ms/step - loss: 0.3605 - accuracy: 0.8602 - val_loss: 0.4858 - val_accuracy: 0.8105
Epoch 150/150
3/3 [==============================] - 0s 83ms/step - loss: 0.5412 - accuracy: 0.7632 - val_loss: 0.4867 - val_accuracy: 0.7895
Epoch 42/150
3/3 [==============================] - 0s 82ms/step - loss: 0.3505 - accuracy: 0.8549 - val_loss: 0.4861 - val_accuracy: 0.8105
3/3 [==============================] - 0s 75ms/step - loss: 0.5403 - accuracy: 0.7526 - val_loss: 0.4851 - val_accuracy: 0.7895
Epoch 43/150
3/3 [==============================] - 0s 53ms/step - loss: 0.4995 - accuracy: 0.7789 - val_loss: 0.4833 - val_accuracy: 0.7895
Epoch 44/150
3/3 [==============================] - 0s 62ms/step - loss: 0.4923 - accuracy: 0.7737 - val_loss: 0.4817 - val_accuracy: 0.7789
Epoch 45/150
3/3 [==============================] - 0s 52ms/step - loss: 0.5194 - accuracy: 0.7816 - val_loss: 0.4805 - val_accuracy: 0.7789
Epoch 46/150
3/3 [==============================] - 0s 56ms/step - loss: 0.4681 - accuracy: 0.8026 - val_loss: 0.4793 - val_accuracy: 0.7789
Epoch 47/150
3/3 [==============================] - 0s 70ms/step - loss: 0.4751 - accuracy: 0.7763 - val_loss: 0.4781 - val_accuracy: 0.7789
Epoch 48/150
3/3 [==============================] - 0s 41ms/step - loss: 0.4866 - accuracy: 0.8105 - val_loss: 0.4767 - val_accuracy: 0.7895
Epoch 49/150
3/3 [==============================] - 0s 54ms/step - loss: 0.5069 - accuracy: 0.7921 - val_loss: 0.4757 - val_accuracy: 0.7895
Epoch 50/150
3/3 [==============================] - 0s 56ms/step - loss: 0.4750 - accuracy: 0.7947 - val_loss: 0.4746 - val_accuracy: 0.7895
Epoch 51/150
3/3 [==============================] - 0s 54ms/step - loss: 0.4716 - accuracy: 0.8079 - val_loss: 0.4736 - val_accuracy: 0.8000
Epoch 52/150
3/3 [==============================] - 0s 48ms/step - loss: 0.4357 - accuracy: 0.8184 - val_loss: 0.4725 - val_accuracy: 0.8000
Epoch 53/150
3/3 [==============================] - 0s 43ms/step - loss: 0.4561 - accuracy: 0.8184 - val_loss: 0.4714 - val_accuracy: 0.7895
Epoch 54/150
3/3 [==============================] - 0s 43ms/step - loss: 0.4913 - accuracy: 0.8079 - val_loss: 0.4704 - val_accuracy: 0.7895
Epoch 55/150
3/3 [==============================] - 0s 43ms/step - loss: 0.4475 - accuracy: 0.8053 - val_loss: 0.4692 - val_accuracy: 0.7895
Epoch 56/150
3/3 [==============================] - 0s 42ms/step - loss: 0.4848 - accuracy: 0.7947 - val_loss: 0.4683 - val_accuracy: 0.7895
Epoch 57/150
3/3 [==============================] - 0s 44ms/step - loss: 0.4872 - accuracy: 0.8000 - val_loss: 0.4674 - val_accuracy: 0.7895
Epoch 58/150
3/3 [==============================] - 0s 51ms/step - loss: 0.4678 - accuracy: 0.7789 - val_loss: 0.4663 - val_accuracy: 0.7895
Epoch 59/150
3/3 [==============================] - 0s 44ms/step - loss: 0.4872 - accuracy: 0.7868 - val_loss: 0.4655 - val_accuracy: 0.7789
Epoch 60/150
3/3 [==============================] - 0s 48ms/step - loss: 0.4330 - accuracy: 0.8026 - val_loss: 0.4647 - val_accuracy: 0.7789
Epoch 61/150
3/3 [==============================] - 0s 44ms/step - loss: 0.4558 - accuracy: 0.7947 - val_loss: 0.4640 - val_accuracy: 0.7789
Epoch 62/150
3/3 [==============================] - 0s 41ms/step - loss: 0.4897 - accuracy: 0.7816 - val_loss: 0.4630 - val_accuracy: 0.7789
Epoch 63/150
3/3 [==============================] - 0s 39ms/step - loss: 0.4643 - accuracy: 0.8105 - val_loss: 0.4624 - val_accuracy: 0.7789
Epoch 64/150
3/3 [==============================] - 0s 63ms/step - loss: 0.5035 - accuracy: 0.7579 - val_loss: 0.4615 - val_accuracy: 0.7789
Epoch 65/150
3/3 [==============================] - 0s 59ms/step - loss: 0.4858 - accuracy: 0.8026 - val_loss: 0.4609 - val_accuracy: 0.7789
Epoch 66/150
3/3 [==============================] - 0s 53ms/step - loss: 0.4425 - accuracy: 0.8079 - val_loss: 0.4600 - val_accuracy: 0.7789
Epoch 67/150
3/3 [==============================] - 0s 59ms/step - loss: 0.4927 - accuracy: 0.8053 - val_loss: 0.4592 - val_accuracy: 0.7789
Epoch 68/150
3/3 [==============================] - 0s 52ms/step - loss: 0.4757 - accuracy: 0.8105 - val_loss: 0.4587 - val_accuracy: 0.7789
Epoch 69/150
3/3 [==============================] - 0s 63ms/step - loss: 0.4416 - accuracy: 0.8026 - val_loss: 0.4580 - val_accuracy: 0.7789
Epoch 70/150
3/3 [==============================] - 0s 50ms/step - loss: 0.4664 - accuracy: 0.7895 - val_loss: 0.4575 - val_accuracy: 0.7789
Epoch 71/150
3/3 [==============================] - 0s 54ms/step - loss: 0.4507 - accuracy: 0.7947 - val_loss: 0.4570 - val_accuracy: 0.7789
Epoch 72/150
3/3 [==============================] - 0s 54ms/step - loss: 0.4936 - accuracy: 0.7947 - val_loss: 0.4562 - val_accuracy: 0.7789
Epoch 73/150
3/3 [==============================] - 0s 58ms/step - loss: 0.4455 - accuracy: 0.8026 - val_loss: 0.4554 - val_accuracy: 0.7789
Epoch 74/150
3/3 [==============================] - 0s 52ms/step - loss: 0.4625 - accuracy: 0.8026 - val_loss: 0.4546 - val_accuracy: 0.7789
Epoch 75/150
3/3 [==============================] - 0s 57ms/step - loss: 0.4725 - accuracy: 0.7895 - val_loss: 0.4539 - val_accuracy: 0.7789
Epoch 76/150
3/3 [==============================] - 0s 54ms/step - loss: 0.4796 - accuracy: 0.7842 - val_loss: 0.4534 - val_accuracy: 0.7789
Epoch 77/150
3/3 [==============================] - 0s 62ms/step - loss: 0.4439 - accuracy: 0.8000 - val_loss: 0.4527 - val_accuracy: 0.7789
Epoch 78/150
3/3 [==============================] - 0s 55ms/step - loss: 0.4885 - accuracy: 0.7974 - val_loss: 0.4523 - val_accuracy: 0.7789
Epoch 79/150
3/3 [==============================] - 0s 63ms/step - loss: 0.4455 - accuracy: 0.8184 - val_loss: 0.4519 - val_accuracy: 0.7789
Epoch 80/150
3/3 [==============================] - 0s 62ms/step - loss: 0.4644 - accuracy: 0.7868 - val_loss: 0.4518 - val_accuracy: 0.7789
Epoch 81/150
3/3 [==============================] - 0s 54ms/step - loss: 0.4504 - accuracy: 0.8237 - val_loss: 0.4516 - val_accuracy: 0.7789
Epoch 82/150
3/3 [==============================] - 0s 45ms/step - loss: 0.4382 - accuracy: 0.8184 - val_loss: 0.4515 - val_accuracy: 0.7789
Epoch 83/150
3/3 [==============================] - 0s 47ms/step - loss: 0.4597 - accuracy: 0.8105 - val_loss: 0.4513 - val_accuracy: 0.7684
Epoch 84/150
3/3 [==============================] - 0s 48ms/step - loss: 0.4443 - accuracy: 0.8132 - val_loss: 0.4512 - val_accuracy: 0.7684
Epoch 85/150
3/3 [==============================] - 0s 44ms/step - loss: 0.4353 - accuracy: 0.8079 - val_loss: 0.4512 - val_accuracy: 0.7684
Epoch 86/150
3/3 [==============================] - 0s 48ms/step - loss: 0.4275 - accuracy: 0.8053 - val_loss: 0.4511 - val_accuracy: 0.7684
Epoch 87/150
3/3 [==============================] - 0s 53ms/step - loss: 0.4153 - accuracy: 0.8132 - val_loss: 0.4512 - val_accuracy: 0.7684
Epoch 88/150
3/3 [==============================] - 0s 43ms/step - loss: 0.4354 - accuracy: 0.8105 - val_loss: 0.4510 - val_accuracy: 0.7684
Epoch 89/150
3/3 [==============================] - 0s 31ms/step - loss: 0.4962 - accuracy: 0.7921 - val_loss: 0.4510 - val_accuracy: 0.7684
Epoch 90/150
3/3 [==============================] - 0s 39ms/step - loss: 0.4471 - accuracy: 0.8105 - val_loss: 0.4509 - val_accuracy: 0.7684
Epoch 91/150
3/3 [==============================] - 0s 32ms/step - loss: 0.4658 - accuracy: 0.8026 - val_loss: 0.4509 - val_accuracy: 0.7684
Epoch 92/150
3/3 [==============================] - 0s 40ms/step - loss: 0.4151 - accuracy: 0.8105 - val_loss: 0.4507 - val_accuracy: 0.7684
Epoch 93/150
3/3 [==============================] - 0s 56ms/step - loss: 0.4131 - accuracy: 0.8316 - val_loss: 0.4505 - val_accuracy: 0.7684
Epoch 94/150
3/3 [==============================] - 0s 51ms/step - loss: 0.4504 - accuracy: 0.8184 - val_loss: 0.4503 - val_accuracy: 0.7684
Epoch 95/150
3/3 [==============================] - 0s 46ms/step - loss: 0.4123 - accuracy: 0.8105 - val_loss: 0.4501 - val_accuracy: 0.7684
Epoch 96/150
3/3 [==============================] - 0s 42ms/step - loss: 0.4554 - accuracy: 0.7974 - val_loss: 0.4503 - val_accuracy: 0.7684
Epoch 97/150
3/3 [==============================] - 0s 47ms/step - loss: 0.4584 - accuracy: 0.8132 - val_loss: 0.4504 - val_accuracy: 0.7684
Epoch 98/150
3/3 [==============================] - 0s 46ms/step - loss: 0.4224 - accuracy: 0.8237 - val_loss: 0.4505 - val_accuracy: 0.7684
Epoch 99/150
3/3 [==============================] - 0s 45ms/step - loss: 0.4317 - accuracy: 0.8184 - val_loss: 0.4504 - val_accuracy: 0.7684
Epoch 100/150
3/3 [==============================] - 0s 47ms/step - loss: 0.4596 - accuracy: 0.8263 - val_loss: 0.4504 - val_accuracy: 0.7684
Epoch 101/150
3/3 [==============================] - 0s 42ms/step - loss: 0.4266 - accuracy: 0.8368 - val_loss: 0.4503 - val_accuracy: 0.7684
Epoch 102/150
3/3 [==============================] - 0s 43ms/step - loss: 0.4646 - accuracy: 0.8053 - val_loss: 0.4502 - val_accuracy: 0.7684
Epoch 103/150
3/3 [==============================] - 0s 38ms/step - loss: 0.3939 - accuracy: 0.8105 - val_loss: 0.4502 - val_accuracy: 0.7684
Epoch 104/150
3/3 [==============================] - 0s 37ms/step - loss: 0.4439 - accuracy: 0.8132 - val_loss: 0.4503 - val_accuracy: 0.7684
Epoch 105/150
3/3 [==============================] - 0s 46ms/step - loss: 0.4380 - accuracy: 0.8184 - val_loss: 0.4505 - val_accuracy: 0.7684
Epoch 106/150
3/3 [==============================] - 0s 39ms/step - loss: 0.4400 - accuracy: 0.8105 - val_loss: 0.4506 - val_accuracy: 0.7684
Epoch 107/150
3/3 [==============================] - 0s 38ms/step - loss: 0.4089 - accuracy: 0.8158 - val_loss: 0.4506 - val_accuracy: 0.7684
Epoch 108/150
3/3 [==============================] - 0s 42ms/step - loss: 0.4126 - accuracy: 0.8211 - val_loss: 0.4507 - val_accuracy: 0.7684
Epoch 109/150
3/3 [==============================] - 0s 43ms/step - loss: 0.4390 - accuracy: 0.8263 - val_loss: 0.4505 - val_accuracy: 0.7684
Epoch 110/150
3/3 [==============================] - 0s 32ms/step - loss: 0.4297 - accuracy: 0.8184 - val_loss: 0.4506 - val_accuracy: 0.7684
Epoch 111/150
3/3 [==============================] - 0s 31ms/step - loss: 0.4588 - accuracy: 0.8184 - val_loss: 0.4505 - val_accuracy: 0.7684
Epoch 112/150
3/3 [==============================] - 0s 35ms/step - loss: 0.4581 - accuracy: 0.8184 - val_loss: 0.4504 - val_accuracy: 0.7684
Epoch 113/150
3/3 [==============================] - 0s 32ms/step - loss: 0.4388 - accuracy: 0.8000 - val_loss: 0.4504 - val_accuracy: 0.7684
Epoch 114/150
3/3 [==============================] - 0s 30ms/step - loss: 0.4492 - accuracy: 0.8158 - val_loss: 0.4505 - val_accuracy: 0.7684
Epoch 115/150
3/3 [==============================] - 0s 40ms/step - loss: 0.4560 - accuracy: 0.8026 - val_loss: 0.4507 - val_accuracy: 0.7684
Epoch 116/150
3/3 [==============================] - 0s 44ms/step - loss: 0.4026 - accuracy: 0.8158 - val_loss: 0.4509 - val_accuracy: 0.7684
Epoch 117/150
3/3 [==============================] - 0s 39ms/step - loss: 0.4517 - accuracy: 0.8105 - val_loss: 0.4512 - val_accuracy: 0.7684
Epoch 118/150
3/3 [==============================] - 0s 39ms/step - loss: 0.4698 - accuracy: 0.7947 - val_loss: 0.4515 - val_accuracy: 0.7684
Epoch 119/150
3/3 [==============================] - 0s 57ms/step - loss: 0.4285 - accuracy: 0.8105 - val_loss: 0.4517 - val_accuracy: 0.7684
Epoch 120/150
3/3 [==============================] - 0s 39ms/step - loss: 0.4232 - accuracy: 0.8263 - val_loss: 0.4520 - val_accuracy: 0.7684
Epoch 121/150
3/3 [==============================] - 0s 41ms/step - loss: 0.4213 - accuracy: 0.8263 - val_loss: 0.4524 - val_accuracy: 0.7684
Epoch 122/150
3/3 [==============================] - 0s 38ms/step - loss: 0.4173 - accuracy: 0.8342 - val_loss: 0.4523 - val_accuracy: 0.7684
Epoch 123/150
3/3 [==============================] - 0s 38ms/step - loss: 0.4288 - accuracy: 0.8053 - val_loss: 0.4524 - val_accuracy: 0.7684
Epoch 124/150
3/3 [==============================] - 0s 39ms/step - loss: 0.4205 - accuracy: 0.8237 - val_loss: 0.4525 - val_accuracy: 0.7684
Epoch 125/150
3/3 [==============================] - 0s 37ms/step - loss: 0.4012 - accuracy: 0.8237 - val_loss: 0.4526 - val_accuracy: 0.7684
Epoch 126/150
3/3 [==============================] - 0s 53ms/step - loss: 0.4401 - accuracy: 0.8105 - val_loss: 0.4526 - val_accuracy: 0.7684
Epoch 127/150
3/3 [==============================] - 0s 43ms/step - loss: 0.4150 - accuracy: 0.8342 - val_loss: 0.4529 - val_accuracy: 0.7684
Epoch 128/150
3/3 [==============================] - 0s 42ms/step - loss: 0.3935 - accuracy: 0.8421 - val_loss: 0.4530 - val_accuracy: 0.7684
Epoch 129/150
3/3 [==============================] - 0s 65ms/step - loss: 0.4169 - accuracy: 0.8184 - val_loss: 0.4533 - val_accuracy: 0.7684
Epoch 130/150
3/3 [==============================] - 0s 48ms/step - loss: 0.4168 - accuracy: 0.8289 - val_loss: 0.4536 - val_accuracy: 0.7684
Epoch 131/150
3/3 [==============================] - 0s 68ms/step - loss: 0.4035 - accuracy: 0.8316 - val_loss: 0.4538 - val_accuracy: 0.7684
Epoch 132/150
3/3 [==============================] - 0s 61ms/step - loss: 0.4097 - accuracy: 0.8237 - val_loss: 0.4538 - val_accuracy: 0.7684
Epoch 133/150
3/3 [==============================] - 0s 49ms/step - loss: 0.4185 - accuracy: 0.8158 - val_loss: 0.4541 - val_accuracy: 0.7684
Epoch 134/150
3/3 [==============================] - 0s 45ms/step - loss: 0.4263 - accuracy: 0.8211 - val_loss: 0.4543 - val_accuracy: 0.7684
Epoch 135/150
3/3 [==============================] - 0s 45ms/step - loss: 0.4414 - accuracy: 0.7921 - val_loss: 0.4545 - val_accuracy: 0.7684
Epoch 136/150
3/3 [==============================] - 0s 47ms/step - loss: 0.4091 - accuracy: 0.8289 - val_loss: 0.4546 - val_accuracy: 0.7684
Epoch 137/150
3/3 [==============================] - 0s 35ms/step - loss: 0.4066 - accuracy: 0.8395 - val_loss: 0.4547 - val_accuracy: 0.7684
Epoch 138/150
3/3 [==============================] - 0s 43ms/step - loss: 0.4071 - accuracy: 0.8395 - val_loss: 0.4548 - val_accuracy: 0.7789
Epoch 139/150
3/3 [==============================] - 0s 45ms/step - loss: 0.4378 - accuracy: 0.8105 - val_loss: 0.4550 - val_accuracy: 0.7789
Epoch 140/150
3/3 [==============================] - 0s 45ms/step - loss: 0.4272 - accuracy: 0.8237 - val_loss: 0.4550 - val_accuracy: 0.7789
Epoch 141/150
3/3 [==============================] - 0s 36ms/step - loss: 0.4417 - accuracy: 0.8289 - val_loss: 0.4552 - val_accuracy: 0.7789
Epoch 142/150
3/3 [==============================] - 0s 39ms/step - loss: 0.4269 - accuracy: 0.8237 - val_loss: 0.4554 - val_accuracy: 0.7789
Epoch 143/150
3/3 [==============================] - 0s 44ms/step - loss: 0.4098 - accuracy: 0.8605 - val_loss: 0.4557 - val_accuracy: 0.7789
Epoch 144/150
3/3 [==============================] - 0s 42ms/step - loss: 0.4159 - accuracy: 0.8316 - val_loss: 0.4560 - val_accuracy: 0.7789
Epoch 145/150
3/3 [==============================] - 0s 43ms/step - loss: 0.4557 - accuracy: 0.8237 - val_loss: 0.4560 - val_accuracy: 0.7789
Epoch 146/150
3/3 [==============================] - 0s 43ms/step - loss: 0.4241 - accuracy: 0.8211 - val_loss: 0.4564 - val_accuracy: 0.7789
Epoch 147/150
3/3 [==============================] - 0s 43ms/step - loss: 0.4199 - accuracy: 0.8263 - val_loss: 0.4567 - val_accuracy: 0.7789
Epoch 148/150
3/3 [==============================] - 0s 35ms/step - loss: 0.4213 - accuracy: 0.8263 - val_loss: 0.4570 - val_accuracy: 0.7789
Epoch 149/150
3/3 [==============================] - 0s 57ms/step - loss: 0.3951 - accuracy: 0.8237 - val_loss: 0.4572 - val_accuracy: 0.7789
Epoch 150/150
3/3 [==============================] - 0s 44ms/step - loss: 0.3783 - accuracy: 0.8447 - val_loss: 0.4575 - val_accuracy: 0.7789
2/2 [==============================] - 0s 11ms/step - loss: 0.3919 - accuracy: 0.8439
Epoch 1/150
3/3 [==============================] - 2s 203ms/step - loss: 0.8349 - accuracy: 0.5132 - val_loss: 0.6689 - val_accuracy: 0.6000
Epoch 2/150
3/3 [==============================] - 0s 41ms/step - loss: 0.8080 - accuracy: 0.5842 - val_loss: 0.6538 - val_accuracy: 0.6421
Epoch 3/150
3/3 [==============================] - 0s 43ms/step - loss: 0.7913 - accuracy: 0.5158 - val_loss: 0.6401 - val_accuracy: 0.6421
Epoch 4/150
3/3 [==============================] - 0s 57ms/step - loss: 0.7121 - accuracy: 0.6368 - val_loss: 0.6279 - val_accuracy: 0.6632
Epoch 5/150
3/3 [==============================] - 0s 53ms/step - loss: 0.6759 - accuracy: 0.6474 - val_loss: 0.6170 - val_accuracy: 0.6842
Epoch 6/150
3/3 [==============================] - 0s 43ms/step - loss: 0.6647 - accuracy: 0.6711 - val_loss: 0.6074 - val_accuracy: 0.7474
Epoch 7/150
3/3 [==============================] - 0s 49ms/step - loss: 0.6397 - accuracy: 0.6868 - val_loss: 0.5985 - val_accuracy: 0.7474
Epoch 8/150
3/3 [==============================] - 0s 43ms/step - loss: 0.6363 - accuracy: 0.6816 - val_loss: 0.5905 - val_accuracy: 0.7474
Epoch 9/150
3/3 [==============================] - 0s 48ms/step - loss: 0.6084 - accuracy: 0.6789 - val_loss: 0.5831 - val_accuracy: 0.7579
Epoch 10/150
3/3 [==============================] - 0s 52ms/step - loss: 0.6168 - accuracy: 0.6763 - val_loss: 0.5760 - val_accuracy: 0.7684
Epoch 11/150
3/3 [==============================] - 0s 45ms/step - loss: 0.5892 - accuracy: 0.7053 - val_loss: 0.5694 - val_accuracy: 0.8000
Epoch 12/150
3/3 [==============================] - 0s 53ms/step - loss: 0.5939 - accuracy: 0.7105 - val_loss: 0.5634 - val_accuracy: 0.8000
Epoch 13/150
3/3 [==============================] - 0s 64ms/step - loss: 0.5985 - accuracy: 0.7079 - val_loss: 0.5573 - val_accuracy: 0.8105
Epoch 14/150
3/3 [==============================] - 0s 45ms/step - loss: 0.6085 - accuracy: 0.7026 - val_loss: 0.5515 - val_accuracy: 0.8211
Epoch 15/150
3/3 [==============================] - 0s 43ms/step - loss: 0.5817 - accuracy: 0.7184 - val_loss: 0.5460 - val_accuracy: 0.8211
Epoch 16/150
3/3 [==============================] - 0s 39ms/step - loss: 0.5608 - accuracy: 0.7421 - val_loss: 0.5406 - val_accuracy: 0.8211
Epoch 17/150
3/3 [==============================] - 0s 37ms/step - loss: 0.5600 - accuracy: 0.7237 - val_loss: 0.5356 - val_accuracy: 0.8211
Epoch 18/150
3/3 [==============================] - 0s 45ms/step - loss: 0.5383 - accuracy: 0.7684 - val_loss: 0.5312 - val_accuracy: 0.8211
Epoch 19/150
3/3 [==============================] - 0s 42ms/step - loss: 0.5059 - accuracy: 0.7526 - val_loss: 0.5269 - val_accuracy: 0.8211
Epoch 20/150
3/3 [==============================] - 0s 41ms/step - loss: 0.5714 - accuracy: 0.7184 - val_loss: 0.5227 - val_accuracy: 0.8421
Epoch 21/150
3/3 [==============================] - 0s 40ms/step - loss: 0.5382 - accuracy: 0.7342 - val_loss: 0.5183 - val_accuracy: 0.8421
Epoch 22/150
3/3 [==============================] - 0s 38ms/step - loss: 0.5122 - accuracy: 0.7658 - val_loss: 0.5141 - val_accuracy: 0.8421
Epoch 23/150
3/3 [==============================] - 0s 45ms/step - loss: 0.5510 - accuracy: 0.7211 - val_loss: 0.5100 - val_accuracy: 0.8421
Epoch 24/150
3/3 [==============================] - 0s 42ms/step - loss: 0.5342 - accuracy: 0.7421 - val_loss: 0.5061 - val_accuracy: 0.8526
Epoch 25/150
3/3 [==============================] - 0s 48ms/step - loss: 0.5521 - accuracy: 0.7500 - val_loss: 0.5025 - val_accuracy: 0.8526
Epoch 26/150
3/3 [==============================] - 0s 44ms/step - loss: 0.5163 - accuracy: 0.7632 - val_loss: 0.4991 - val_accuracy: 0.8526
Epoch 27/150
3/3 [==============================] - 0s 42ms/step - loss: 0.5353 - accuracy: 0.7632 - val_loss: 0.4955 - val_accuracy: 0.8526
Epoch 28/150
3/3 [==============================] - 0s 42ms/step - loss: 0.5351 - accuracy: 0.7395 - val_loss: 0.4919 - val_accuracy: 0.8632
Epoch 29/150
3/3 [==============================] - 0s 43ms/step - loss: 0.5142 - accuracy: 0.7632 - val_loss: 0.4883 - val_accuracy: 0.8632
Epoch 30/150
3/3 [==============================] - 0s 63ms/step - loss: 0.5295 - accuracy: 0.7526 - val_loss: 0.4849 - val_accuracy: 0.8632
Epoch 31/150
3/3 [==============================] - 0s 50ms/step - loss: 0.4984 - accuracy: 0.7579 - val_loss: 0.4817 - val_accuracy: 0.8526
Epoch 32/150
3/3 [==============================] - 0s 50ms/step - loss: 0.5268 - accuracy: 0.7605 - val_loss: 0.4782 - val_accuracy: 0.8526
Epoch 33/150
3/3 [==============================] - 0s 46ms/step - loss: 0.4880 - accuracy: 0.7842 - val_loss: 0.4747 - val_accuracy: 0.8526
Epoch 34/150
3/3 [==============================] - 0s 56ms/step - loss: 0.4903 - accuracy: 0.7947 - val_loss: 0.4715 - val_accuracy: 0.8526
Epoch 35/150
3/3 [==============================] - 0s 55ms/step - loss: 0.5068 - accuracy: 0.7500 - val_loss: 0.4688 - val_accuracy: 0.8526
Epoch 36/150
3/3 [==============================] - 0s 64ms/step - loss: 0.5092 - accuracy: 0.7526 - val_loss: 0.4659 - val_accuracy: 0.8632
Epoch 37/150
3/3 [==============================] - 0s 50ms/step - loss: 0.5151 - accuracy: 0.7605 - val_loss: 0.4629 - val_accuracy: 0.8632
Epoch 38/150
3/3 [==============================] - 0s 52ms/step - loss: 0.5041 - accuracy: 0.7789 - val_loss: 0.4599 - val_accuracy: 0.8632
Epoch 39/150
3/3 [==============================] - 0s 74ms/step - loss: 0.5103 - accuracy: 0.7632 - val_loss: 0.4571 - val_accuracy: 0.8632
Epoch 40/150
3/3 [==============================] - 0s 71ms/step - loss: 0.5216 - accuracy: 0.7421 - val_loss: 0.4542 - val_accuracy: 0.8632
Epoch 41/150
2/2 [==============================] - 0s 9ms/step - loss: 0.5573 - accuracy: 0.7773
3/3 [==============================] - 0s 76ms/step - loss: 0.4512 - accuracy: 0.7868 - val_loss: 0.4517 - val_accuracy: 0.8632
Epoch 42/150
3/3 [==============================] - 0s 71ms/step - loss: 0.5182 - accuracy: 0.7658 - val_loss: 0.4491 - val_accuracy: 0.8632
Epoch 43/150
3/3 [==============================] - 0s 64ms/step - loss: 0.4913 - accuracy: 0.7763 - val_loss: 0.4461 - val_accuracy: 0.8632
Epoch 44/150
3/3 [==============================] - 0s 72ms/step - loss: 0.5378 - accuracy: 0.7737 - val_loss: 0.4434 - val_accuracy: 0.8632
Epoch 45/150
1/3 [=========>....................] - ETA: 0s - loss: 0.4955 - accuracy: 0.7422Epoch 1/150
3/3 [==============================] - 0s 59ms/step - loss: 0.5027 - accuracy: 0.7579 - val_loss: 0.4407 - val_accuracy: 0.8632
Epoch 46/150
3/3 [==============================] - 0s 55ms/step - loss: 0.5140 - accuracy: 0.7658 - val_loss: 0.4382 - val_accuracy: 0.8632
Epoch 47/150
3/3 [==============================] - 0s 67ms/step - loss: 0.4568 - accuracy: 0.8053 - val_loss: 0.4359 - val_accuracy: 0.8632
Epoch 48/150
3/3 [==============================] - 0s 62ms/step - loss: 0.4943 - accuracy: 0.7763 - val_loss: 0.4335 - val_accuracy: 0.8632
Epoch 49/150
3/3 [==============================] - 0s 69ms/step - loss: 0.4767 - accuracy: 0.7789 - val_loss: 0.4314 - val_accuracy: 0.8632
Epoch 50/150
3/3 [==============================] - 0s 69ms/step - loss: 0.4768 - accuracy: 0.8026 - val_loss: 0.4292 - val_accuracy: 0.8632
Epoch 51/150
3/3 [==============================] - 0s 72ms/step - loss: 0.5052 - accuracy: 0.7684 - val_loss: 0.4274 - val_accuracy: 0.8632
Epoch 52/150
3/3 [==============================] - 0s 88ms/step - loss: 0.4402 - accuracy: 0.7895 - val_loss: 0.4254 - val_accuracy: 0.8632
Epoch 53/150
3/3 [==============================] - 0s 64ms/step - loss: 0.4867 - accuracy: 0.7658 - val_loss: 0.4233 - val_accuracy: 0.8632
Epoch 54/150
3/3 [==============================] - 0s 81ms/step - loss: 0.5019 - accuracy: 0.7868 - val_loss: 0.4215 - val_accuracy: 0.8526
Epoch 55/150
3/3 [==============================] - 0s 80ms/step - loss: 0.5056 - accuracy: 0.7605 - val_loss: 0.4196 - val_accuracy: 0.8526
Epoch 56/150
3/3 [==============================] - 0s 69ms/step - loss: 0.4670 - accuracy: 0.7868 - val_loss: 0.4177 - val_accuracy: 0.8526
Epoch 57/150
3/3 [==============================] - 0s 64ms/step - loss: 0.5136 - accuracy: 0.7447 - val_loss: 0.4160 - val_accuracy: 0.8526
Epoch 58/150
3/3 [==============================] - 0s 63ms/step - loss: 0.4993 - accuracy: 0.7605 - val_loss: 0.4147 - val_accuracy: 0.8526
Epoch 59/150
3/3 [==============================] - 0s 56ms/step - loss: 0.4520 - accuracy: 0.7868 - val_loss: 0.4130 - val_accuracy: 0.8526
Epoch 60/150
3/3 [==============================] - 0s 55ms/step - loss: 0.4652 - accuracy: 0.7553 - val_loss: 0.4113 - val_accuracy: 0.8526
Epoch 61/150
3/3 [==============================] - 0s 56ms/step - loss: 0.4927 - accuracy: 0.7816 - val_loss: 0.4094 - val_accuracy: 0.8526
Epoch 62/150
3/3 [==============================] - 0s 64ms/step - loss: 0.4588 - accuracy: 0.7789 - val_loss: 0.4076 - val_accuracy: 0.8632
Epoch 63/150
3/3 [==============================] - 0s 67ms/step - loss: 0.4801 - accuracy: 0.7895 - val_loss: 0.4061 - val_accuracy: 0.8632
Epoch 64/150
3/3 [==============================] - 0s 66ms/step - loss: 0.4877 - accuracy: 0.7789 - val_loss: 0.4046 - val_accuracy: 0.8632
Epoch 65/150
3/3 [==============================] - 0s 71ms/step - loss: 0.4663 - accuracy: 0.7842 - val_loss: 0.4033 - val_accuracy: 0.8632
Epoch 66/150
3/3 [==============================] - 0s 68ms/step - loss: 0.4904 - accuracy: 0.7553 - val_loss: 0.4017 - val_accuracy: 0.8632
Epoch 67/150
3/3 [==============================] - 0s 77ms/step - loss: 0.4550 - accuracy: 0.8158 - val_loss: 0.4006 - val_accuracy: 0.8632
Epoch 68/150
3/3 [==============================] - 0s 68ms/step - loss: 0.4650 - accuracy: 0.7737 - val_loss: 0.3994 - val_accuracy: 0.8632
Epoch 69/150
3/3 [==============================] - 0s 64ms/step - loss: 0.4806 - accuracy: 0.7763 - val_loss: 0.3979 - val_accuracy: 0.8632
Epoch 70/150
3/3 [==============================] - 0s 73ms/step - loss: 0.4386 - accuracy: 0.7842 - val_loss: 0.3966 - val_accuracy: 0.8632
Epoch 71/150
3/3 [==============================] - 0s 67ms/step - loss: 0.4281 - accuracy: 0.8053 - val_loss: 0.3950 - val_accuracy: 0.8632
Epoch 72/150
3/3 [==============================] - 0s 81ms/step - loss: 0.4365 - accuracy: 0.8079 - val_loss: 0.3938 - val_accuracy: 0.8632
Epoch 73/150
3/3 [==============================] - 0s 84ms/step - loss: 0.4288 - accuracy: 0.8211 - val_loss: 0.3927 - val_accuracy: 0.8632
Epoch 74/150
3/3 [==============================] - 0s 75ms/step - loss: 0.4789 - accuracy: 0.8026 - val_loss: 0.3914 - val_accuracy: 0.8632
Epoch 75/150
3/3 [==============================] - 0s 73ms/step - loss: 0.4605 - accuracy: 0.7763 - val_loss: 0.3905 - val_accuracy: 0.8632
Epoch 76/150
3/3 [==============================] - 0s 85ms/step - loss: 0.4750 - accuracy: 0.7974 - val_loss: 0.3896 - val_accuracy: 0.8632
Epoch 77/150
3/3 [==============================] - 0s 63ms/step - loss: 0.4858 - accuracy: 0.7868 - val_loss: 0.3887 - val_accuracy: 0.8632
Epoch 78/150
3/3 [==============================] - 0s 68ms/step - loss: 0.4452 - accuracy: 0.7868 - val_loss: 0.3878 - val_accuracy: 0.8632
Epoch 79/150
3/3 [==============================] - 0s 63ms/step - loss: 0.4632 - accuracy: 0.7947 - val_loss: 0.3869 - val_accuracy: 0.8632
Epoch 80/150
3/3 [==============================] - 0s 69ms/step - loss: 0.4599 - accuracy: 0.8053 - val_loss: 0.3861 - val_accuracy: 0.8632
Epoch 81/150
3/3 [==============================] - 6s 517ms/step - loss: 0.9638 - accuracy: 0.5356 - val_loss: 0.7374 - val_accuracy: 0.4526
1/3 [=========>....................] - ETA: 0s - loss: 0.4806 - accuracy: 0.8047Epoch 2/150
3/3 [==============================] - 0s 79ms/step - loss: 0.4770 - accuracy: 0.7921 - val_loss: 0.3853 - val_accuracy: 0.8632
Epoch 82/150
3/3 [==============================] - 0s 67ms/step - loss: 0.8636 - accuracy: 0.5541 - val_loss: 0.7131 - val_accuracy: 0.5474
Epoch 3/150
3/3 [==============================] - 0s 81ms/step - loss: 0.4707 - accuracy: 0.7842 - val_loss: 0.3844 - val_accuracy: 0.8632
3/3 [==============================] - 0s 80ms/step - loss: 0.7862 - accuracy: 0.5884 - val_loss: 0.6922 - val_accuracy: 0.5895
Epoch 83/150
Epoch 4/150
3/3 [==============================] - 0s 61ms/step - loss: 0.4826 - accuracy: 0.7868 - val_loss: 0.3838 - val_accuracy: 0.8632
3/3 [==============================] - 0s 60ms/step - loss: 0.6818 - accuracy: 0.6306 - val_loss: 0.6734 - val_accuracy: 0.6211
Epoch 5/150
Epoch 84/150
3/3 [==============================] - 0s 71ms/step - loss: 0.6601 - accuracy: 0.6939 - val_loss: 0.6572 - val_accuracy: 0.6316
Epoch 6/150
3/3 [==============================] - 0s 71ms/step - loss: 0.4824 - accuracy: 0.7579 - val_loss: 0.3831 - val_accuracy: 0.8632
Epoch 85/150
3/3 [==============================] - 0s 65ms/step - loss: 0.6485 - accuracy: 0.7071 - val_loss: 0.6423 - val_accuracy: 0.6526
Epoch 7/150
3/3 [==============================] - 0s 90ms/step - loss: 0.4624 - accuracy: 0.7711 - val_loss: 0.3827 - val_accuracy: 0.8632
Epoch 86/150
3/3 [==============================] - 0s 91ms/step - loss: 0.5284 - accuracy: 0.7599 - val_loss: 0.6290 - val_accuracy: 0.6842
Epoch 8/150
3/3 [==============================] - 0s 85ms/step - loss: 0.4710 - accuracy: 0.7895 - val_loss: 0.3822 - val_accuracy: 0.8632
Epoch 87/150
3/3 [==============================] - 0s 68ms/step - loss: 0.5836 - accuracy: 0.7309 - val_loss: 0.6168 - val_accuracy: 0.6842
Epoch 9/150
3/3 [==============================] - 0s 66ms/step - loss: 0.4318 - accuracy: 0.8079 - val_loss: 0.3817 - val_accuracy: 0.8632
Epoch 88/150
3/3 [==============================] - 0s 81ms/step - loss: 0.5759 - accuracy: 0.7282 - val_loss: 0.6051 - val_accuracy: 0.6947
Epoch 10/150
3/3 [==============================] - 0s 92ms/step - loss: 0.4725 - accuracy: 0.7868 - val_loss: 0.3811 - val_accuracy: 0.8632
Epoch 89/150
3/3 [==============================] - 0s 74ms/step - loss: 0.5391 - accuracy: 0.7414 - val_loss: 0.5940 - val_accuracy: 0.7053
Epoch 11/150
3/3 [==============================] - 0s 61ms/step - loss: 0.4625 - accuracy: 0.7921 - val_loss: 0.3803 - val_accuracy: 0.8632
Epoch 90/150
3/3 [==============================] - 0s 69ms/step - loss: 0.5111 - accuracy: 0.7652 - val_loss: 0.5841 - val_accuracy: 0.7368
Epoch 12/150
3/3 [==============================] - 0s 77ms/step - loss: 0.4743 - accuracy: 0.7974 - val_loss: 0.3796 - val_accuracy: 0.8632
Epoch 91/150
3/3 [==============================] - 0s 50ms/step - loss: 0.4754 - accuracy: 0.7625 - val_loss: 0.5749 - val_accuracy: 0.7474
Epoch 13/150
3/3 [==============================] - 0s 67ms/step - loss: 0.4800 - accuracy: 0.7816 - val_loss: 0.3789 - val_accuracy: 0.8632
Epoch 92/150
3/3 [==============================] - 0s 82ms/step - loss: 0.4580 - accuracy: 0.7889 - val_loss: 0.5665 - val_accuracy: 0.7474
Epoch 14/150
3/3 [==============================] - 0s 52ms/step - loss: 0.4917 - accuracy: 0.8026 - val_loss: 0.3785 - val_accuracy: 0.8632
Epoch 93/150
3/3 [==============================] - 0s 68ms/step - loss: 0.5023 - accuracy: 0.7625 - val_loss: 0.5585 - val_accuracy: 0.7579
Epoch 15/150
3/3 [==============================] - 0s 71ms/step - loss: 0.4405 - accuracy: 0.8026 - val_loss: 0.3776 - val_accuracy: 0.8632
Epoch 94/150
3/3 [==============================] - 0s 77ms/step - loss: 0.4855 - accuracy: 0.7652 - val_loss: 0.5509 - val_accuracy: 0.7579
Epoch 16/150
3/3 [==============================] - 0s 69ms/step - loss: 0.4801 - accuracy: 0.7816 - val_loss: 0.3768 - val_accuracy: 0.8632
Epoch 95/150
3/3 [==============================] - 0s 75ms/step - loss: 0.4990 - accuracy: 0.7731 - val_loss: 0.5438 - val_accuracy: 0.7684
Epoch 17/150
3/3 [==============================] - 0s 72ms/step - loss: 0.4241 - accuracy: 0.8132 - val_loss: 0.3760 - val_accuracy: 0.8632
Epoch 96/150
3/3 [==============================] - 0s 66ms/step - loss: 0.4606 - accuracy: 0.7810 - val_loss: 0.5376 - val_accuracy: 0.7684
Epoch 18/150
3/3 [==============================] - 0s 57ms/step - loss: 0.4662 - accuracy: 0.7842 - val_loss: 0.3752 - val_accuracy: 0.8632
Epoch 97/150
3/3 [==============================] - 0s 67ms/step - loss: 0.4260 - accuracy: 0.7889 - val_loss: 0.5316 - val_accuracy: 0.7895
Epoch 19/150
3/3 [==============================] - 0s 64ms/step - loss: 0.4629 - accuracy: 0.8000 - val_loss: 0.3746 - val_accuracy: 0.8632
Epoch 98/150
3/3 [==============================] - 0s 53ms/step - loss: 0.4567 - accuracy: 0.7868 - val_loss: 0.3741 - val_accuracy: 0.8632
Epoch 99/150
3/3 [==============================] - 0s 74ms/step - loss: 0.4761 - accuracy: 0.7889 - val_loss: 0.5261 - val_accuracy: 0.7895
Epoch 20/150
3/3 [==============================] - 0s 59ms/step - loss: 0.4593 - accuracy: 0.7995 - val_loss: 0.5207 - val_accuracy: 0.7895
Epoch 21/150
3/3 [==============================] - 0s 78ms/step - loss: 0.4799 - accuracy: 0.7816 - val_loss: 0.3734 - val_accuracy: 0.8632
Epoch 100/150
3/3 [==============================] - 0s 67ms/step - loss: 0.4365 - accuracy: 0.7968 - val_loss: 0.5157 - val_accuracy: 0.7895
Epoch 22/150
3/3 [==============================] - 0s 66ms/step - loss: 0.4656 - accuracy: 0.7974 - val_loss: 0.3728 - val_accuracy: 0.8632
1/3 [=========>....................] - ETA: 0s - loss: 0.4022 - accuracy: 0.8125Epoch 101/150
3/3 [==============================] - 0s 71ms/step - loss: 0.4315 - accuracy: 0.8127 - val_loss: 0.5109 - val_accuracy: 0.8105
Epoch 23/150
3/3 [==============================] - 0s 67ms/step - loss: 0.4362 - accuracy: 0.8053 - val_loss: 0.3723 - val_accuracy: 0.8632
Epoch 102/150
3/3 [==============================] - 0s 59ms/step - loss: 0.4505 - accuracy: 0.8053 - val_loss: 0.3720 - val_accuracy: 0.8632
3/3 [==============================] - 0s 71ms/step - loss: 0.4425 - accuracy: 0.8100 - val_loss: 0.5063 - val_accuracy: 0.8105
Epoch 24/150
Epoch 103/150
3/3 [==============================] - 0s 67ms/step - loss: 0.4570 - accuracy: 0.7968 - val_loss: 0.5021 - val_accuracy: 0.8105
Epoch 25/150
3/3 [==============================] - 0s 64ms/step - loss: 0.4754 - accuracy: 0.7789 - val_loss: 0.3715 - val_accuracy: 0.8632
Epoch 104/150
3/3 [==============================] - 0s 72ms/step - loss: 0.4336 - accuracy: 0.8074 - val_loss: 0.4984 - val_accuracy: 0.8211
Epoch 26/150
3/3 [==============================] - 0s 67ms/step - loss: 0.4588 - accuracy: 0.7842 - val_loss: 0.3710 - val_accuracy: 0.8632
Epoch 105/150
3/3 [==============================] - 0s 61ms/step - loss: 0.3904 - accuracy: 0.8364 - val_loss: 0.4952 - val_accuracy: 0.8105
Epoch 27/150
3/3 [==============================] - 0s 66ms/step - loss: 0.4308 - accuracy: 0.8079 - val_loss: 0.3703 - val_accuracy: 0.8632
Epoch 106/150
3/3 [==============================] - 0s 54ms/step - loss: 0.4489 - accuracy: 0.7711 - val_loss: 0.3697 - val_accuracy: 0.8632
Epoch 107/150
3/3 [==============================] - 0s 62ms/step - loss: 0.4355 - accuracy: 0.8047 - val_loss: 0.4919 - val_accuracy: 0.8105
Epoch 28/150
3/3 [==============================] - 0s 57ms/step - loss: 0.4432 - accuracy: 0.7816 - val_loss: 0.3689 - val_accuracy: 0.8632
Epoch 108/150
3/3 [==============================] - 0s 63ms/step - loss: 0.4239 - accuracy: 0.8021 - val_loss: 0.4889 - val_accuracy: 0.8105
Epoch 29/150
3/3 [==============================] - 0s 66ms/step - loss: 0.4338 - accuracy: 0.8179 - val_loss: 0.4860 - val_accuracy: 0.8105
3/3 [==============================] - 0s 68ms/step - loss: 0.4655 - accuracy: 0.7842 - val_loss: 0.3684 - val_accuracy: 0.8632
Epoch 30/150
Epoch 109/150
3/3 [==============================] - 0s 87ms/step - loss: 0.4222 - accuracy: 0.8127 - val_loss: 0.4835 - val_accuracy: 0.8105
3/3 [==============================] - 0s 92ms/step - loss: 0.4551 - accuracy: 0.7947 - val_loss: 0.3679 - val_accuracy: 0.8632
Epoch 31/150
Epoch 110/150
3/3 [==============================] - 0s 101ms/step - loss: 0.4634 - accuracy: 0.7868 - val_loss: 0.3676 - val_accuracy: 0.8632
3/3 [==============================] - 0s 102ms/step - loss: 0.4187 - accuracy: 0.8232 - val_loss: 0.4810 - val_accuracy: 0.8105
Epoch 32/150
Epoch 111/150
3/3 [==============================] - 0s 79ms/step - loss: 0.4132 - accuracy: 0.8153 - val_loss: 0.4786 - val_accuracy: 0.8105
Epoch 33/150
3/3 [==============================] - 0s 89ms/step - loss: 0.4352 - accuracy: 0.8000 - val_loss: 0.3673 - val_accuracy: 0.8632
Epoch 112/150
3/3 [==============================] - 0s 86ms/step - loss: 0.3796 - accuracy: 0.8391 - val_loss: 0.4766 - val_accuracy: 0.8105
Epoch 34/150
3/3 [==============================] - 0s 71ms/step - loss: 0.4536 - accuracy: 0.7947 - val_loss: 0.3671 - val_accuracy: 0.8632
Epoch 113/150
3/3 [==============================] - 0s 73ms/step - loss: 0.3955 - accuracy: 0.8232 - val_loss: 0.4744 - val_accuracy: 0.8105
Epoch 35/150
3/3 [==============================] - 0s 68ms/step - loss: 0.4171 - accuracy: 0.8132 - val_loss: 0.3669 - val_accuracy: 0.8632
Epoch 114/150
3/3 [==============================] - 0s 55ms/step - loss: 0.4366 - accuracy: 0.8184 - val_loss: 0.3665 - val_accuracy: 0.8632
Epoch 115/150
3/3 [==============================] - 0s 102ms/step - loss: 0.4092 - accuracy: 0.8127 - val_loss: 0.4724 - val_accuracy: 0.8105
Epoch 36/150
3/3 [==============================] - 0s 78ms/step - loss: 0.4194 - accuracy: 0.7921 - val_loss: 0.3659 - val_accuracy: 0.8632
Epoch 116/150
3/3 [==============================] - 0s 75ms/step - loss: 0.3969 - accuracy: 0.8179 - val_loss: 0.4703 - val_accuracy: 0.8105
Epoch 37/150
3/3 [==============================] - 0s 82ms/step - loss: 0.4628 - accuracy: 0.7921 - val_loss: 0.3652 - val_accuracy: 0.8632
Epoch 117/150
3/3 [==============================] - 0s 67ms/step - loss: 0.4274 - accuracy: 0.8153 - val_loss: 0.4687 - val_accuracy: 0.8211
Epoch 38/150
3/3 [==============================] - 0s 63ms/step - loss: 0.4093 - accuracy: 0.8179 - val_loss: 0.4672 - val_accuracy: 0.8105
Epoch 39/150
3/3 [==============================] - 0s 71ms/step - loss: 0.4409 - accuracy: 0.8132 - val_loss: 0.3647 - val_accuracy: 0.8632
Epoch 118/150
3/3 [==============================] - 0s 69ms/step - loss: 0.4305 - accuracy: 0.8153 - val_loss: 0.4657 - val_accuracy: 0.8211
Epoch 40/150
3/3 [==============================] - 0s 73ms/step - loss: 0.4583 - accuracy: 0.7947 - val_loss: 0.3643 - val_accuracy: 0.8632
Epoch 119/150
3/3 [==============================] - 0s 54ms/step - loss: 0.3726 - accuracy: 0.8391 - val_loss: 0.4644 - val_accuracy: 0.8105
Epoch 41/150
3/3 [==============================] - 0s 79ms/step - loss: 0.4362 - accuracy: 0.8132 - val_loss: 0.3639 - val_accuracy: 0.8632
Epoch 120/150
3/3 [==============================] - 0s 116ms/step - loss: 0.3780 - accuracy: 0.8391 - val_loss: 0.4634 - val_accuracy: 0.8105
Epoch 42/150
3/3 [==============================] - 0s 91ms/step - loss: 0.4583 - accuracy: 0.7974 - val_loss: 0.3638 - val_accuracy: 0.8632
1/3 [=========>....................] - ETA: 0s - loss: 0.3756 - accuracy: 0.8438Epoch 121/150
3/3 [==============================] - 0s 79ms/step - loss: 0.4058 - accuracy: 0.8232 - val_loss: 0.4622 - val_accuracy: 0.8105
Epoch 43/150
3/3 [==============================] - 0s 84ms/step - loss: 0.4415 - accuracy: 0.7921 - val_loss: 0.3635 - val_accuracy: 0.8632
Epoch 122/150
3/3 [==============================] - 0s 127ms/step - loss: 0.4012 - accuracy: 0.8179 - val_loss: 0.4611 - val_accuracy: 0.8105
Epoch 44/150
3/3 [==============================] - 0s 105ms/step - loss: 0.4396 - accuracy: 0.7921 - val_loss: 0.3634 - val_accuracy: 0.8632
Epoch 123/150
3/3 [==============================] - 0s 87ms/step - loss: 0.4428 - accuracy: 0.7895 - val_loss: 0.3631 - val_accuracy: 0.8632
Epoch 124/150
3/3 [==============================] - 0s 119ms/step - loss: 0.3878 - accuracy: 0.8364 - val_loss: 0.4602 - val_accuracy: 0.8211
Epoch 45/150
3/3 [==============================] - 0s 69ms/step - loss: 0.4647 - accuracy: 0.7974 - val_loss: 0.3629 - val_accuracy: 0.8632
Epoch 125/150
3/3 [==============================] - 0s 63ms/step - loss: 0.3803 - accuracy: 0.8443 - val_loss: 0.4595 - val_accuracy: 0.8211
Epoch 46/150
3/3 [==============================] - 0s 72ms/step - loss: 0.4399 - accuracy: 0.7711 - val_loss: 0.3628 - val_accuracy: 0.8632
3/3 [==============================] - 0s 58ms/step - loss: 0.4202 - accuracy: 0.8338 - val_loss: 0.4589 - val_accuracy: 0.8211
Epoch 126/150
Epoch 47/150
3/3 [==============================] - 0s 64ms/step - loss: 0.3997 - accuracy: 0.8285 - val_loss: 0.4582 - val_accuracy: 0.8211
3/3 [==============================] - 0s 62ms/step - loss: 0.4113 - accuracy: 0.8132 - val_loss: 0.3627 - val_accuracy: 0.8632
Epoch 48/150
1/3 [=========>....................] - ETA: 0s - loss: 0.3933 - accuracy: 0.8125Epoch 127/150
3/3 [==============================] - 0s 66ms/step - loss: 0.4122 - accuracy: 0.8311 - val_loss: 0.4577 - val_accuracy: 0.8211
Epoch 49/150
3/3 [==============================] - 0s 75ms/step - loss: 0.4779 - accuracy: 0.7868 - val_loss: 0.3625 - val_accuracy: 0.8632
Epoch 128/150
3/3 [==============================] - 0s 81ms/step - loss: 0.3818 - accuracy: 0.8338 - val_loss: 0.4574 - val_accuracy: 0.8211
Epoch 50/150
3/3 [==============================] - 0s 68ms/step - loss: 0.4222 - accuracy: 0.8000 - val_loss: 0.3621 - val_accuracy: 0.8632
Epoch 129/150
3/3 [==============================] - 0s 72ms/step - loss: 0.3793 - accuracy: 0.8417 - val_loss: 0.4572 - val_accuracy: 0.8211
Epoch 51/150
3/3 [==============================] - 0s 74ms/step - loss: 0.4441 - accuracy: 0.7842 - val_loss: 0.3619 - val_accuracy: 0.8632
Epoch 130/150
3/3 [==============================] - 0s 86ms/step - loss: 0.3993 - accuracy: 0.8338 - val_loss: 0.4571 - val_accuracy: 0.8211
Epoch 52/150
3/3 [==============================] - 0s 71ms/step - loss: 0.4369 - accuracy: 0.8105 - val_loss: 0.3617 - val_accuracy: 0.8632
Epoch 131/150
3/3 [==============================] - 0s 77ms/step - loss: 0.3875 - accuracy: 0.8127 - val_loss: 0.4568 - val_accuracy: 0.8211
Epoch 53/150
3/3 [==============================] - 0s 88ms/step - loss: 0.4572 - accuracy: 0.7842 - val_loss: 0.3613 - val_accuracy: 0.8632
Epoch 132/150
3/3 [==============================] - 0s 95ms/step - loss: 0.3906 - accuracy: 0.8338 - val_loss: 0.4565 - val_accuracy: 0.8211
Epoch 54/150
3/3 [==============================] - 0s 91ms/step - loss: 0.4550 - accuracy: 0.7868 - val_loss: 0.3610 - val_accuracy: 0.8632
Epoch 133/150
3/3 [==============================] - 0s 61ms/step - loss: 0.3323 - accuracy: 0.8549 - val_loss: 0.4564 - val_accuracy: 0.8211
Epoch 55/150
3/3 [==============================] - 0s 136ms/step - loss: 0.4191 - accuracy: 0.8079 - val_loss: 0.3608 - val_accuracy: 0.8632
3/3 [==============================] - 0s 75ms/step - loss: 0.3700 - accuracy: 0.8364 - val_loss: 0.4562 - val_accuracy: 0.8211
Epoch 134/150
Epoch 56/150
3/3 [==============================] - 0s 67ms/step - loss: 0.4283 - accuracy: 0.8079 - val_loss: 0.3608 - val_accuracy: 0.8632
Epoch 135/150
3/3 [==============================] - 0s 119ms/step - loss: 0.3704 - accuracy: 0.8391 - val_loss: 0.4559 - val_accuracy: 0.8211
Epoch 57/150
3/3 [==============================] - 0s 110ms/step - loss: 0.4235 - accuracy: 0.8105 - val_loss: 0.3609 - val_accuracy: 0.8632
Epoch 136/150
3/3 [==============================] - 0s 87ms/step - loss: 0.3787 - accuracy: 0.8496 - val_loss: 0.4558 - val_accuracy: 0.8211
Epoch 58/150
3/3 [==============================] - 0s 106ms/step - loss: 0.4063 - accuracy: 0.8342 - val_loss: 0.3607 - val_accuracy: 0.8632
Epoch 137/150
3/3 [==============================] - 0s 103ms/step - loss: 0.3679 - accuracy: 0.8417 - val_loss: 0.4557 - val_accuracy: 0.8211
Epoch 59/150
3/3 [==============================] - 0s 95ms/step - loss: 0.4459 - accuracy: 0.8158 - val_loss: 0.3606 - val_accuracy: 0.8632
Epoch 138/150
3/3 [==============================] - 0s 100ms/step - loss: 0.3681 - accuracy: 0.8391 - val_loss: 0.4557 - val_accuracy: 0.8211
Epoch 60/150
3/3 [==============================] - 0s 79ms/step - loss: 0.4297 - accuracy: 0.8079 - val_loss: 0.3604 - val_accuracy: 0.8632
Epoch 139/150
3/3 [==============================] - 0s 95ms/step - loss: 0.3897 - accuracy: 0.8522 - val_loss: 0.4558 - val_accuracy: 0.8211
Epoch 61/150
3/3 [==============================] - 0s 76ms/step - loss: 0.4573 - accuracy: 0.7684 - val_loss: 0.3603 - val_accuracy: 0.8632
Epoch 140/150
3/3 [==============================] - 0s 51ms/step - loss: 0.4609 - accuracy: 0.8132 - val_loss: 0.3607 - val_accuracy: 0.8632
Epoch 141/150
3/3 [==============================] - 0s 103ms/step - loss: 0.4161 - accuracy: 0.8311 - val_loss: 0.4561 - val_accuracy: 0.8211
Epoch 62/150
3/3 [==============================] - 0s 76ms/step - loss: 0.4127 - accuracy: 0.8184 - val_loss: 0.3606 - val_accuracy: 0.8632
Epoch 142/150
3/3 [==============================] - 0s 82ms/step - loss: 0.4177 - accuracy: 0.8105 - val_loss: 0.3605 - val_accuracy: 0.8632
3/3 [==============================] - 0s 131ms/step - loss: 0.3694 - accuracy: 0.8496 - val_loss: 0.4563 - val_accuracy: 0.8211
Epoch 63/150
Epoch 143/150
3/3 [==============================] - 0s 73ms/step - loss: 0.3817 - accuracy: 0.8391 - val_loss: 0.4562 - val_accuracy: 0.8211
Epoch 64/150
3/3 [==============================] - 0s 106ms/step - loss: 0.4089 - accuracy: 0.8026 - val_loss: 0.3605 - val_accuracy: 0.8632
Epoch 144/150
3/3 [==============================] - 0s 105ms/step - loss: 0.3615 - accuracy: 0.8575 - val_loss: 0.4565 - val_accuracy: 0.8211
Epoch 65/150
3/3 [==============================] - 0s 97ms/step - loss: 0.4367 - accuracy: 0.8000 - val_loss: 0.3603 - val_accuracy: 0.8632
Epoch 145/150
3/3 [==============================] - 0s 59ms/step - loss: 0.3550 - accuracy: 0.8681 - val_loss: 0.4568 - val_accuracy: 0.8211
Epoch 66/150
3/3 [==============================] - 0s 69ms/step - loss: 0.4421 - accuracy: 0.7921 - val_loss: 0.3603 - val_accuracy: 0.8632
Epoch 146/150
3/3 [==============================] - 0s 68ms/step - loss: 0.3552 - accuracy: 0.8443 - val_loss: 0.4568 - val_accuracy: 0.8211
Epoch 67/150
3/3 [==============================] - 0s 68ms/step - loss: 0.4376 - accuracy: 0.8105 - val_loss: 0.3602 - val_accuracy: 0.8632
Epoch 147/150
3/3 [==============================] - 0s 60ms/step - loss: 0.3659 - accuracy: 0.8311 - val_loss: 0.4570 - val_accuracy: 0.8211
Epoch 68/150
3/3 [==============================] - 0s 75ms/step - loss: 0.4359 - accuracy: 0.7974 - val_loss: 0.3602 - val_accuracy: 0.8632
Epoch 148/150
3/3 [==============================] - 0s 83ms/step - loss: 0.3520 - accuracy: 0.8628 - val_loss: 0.4572 - val_accuracy: 0.8211
Epoch 69/150
3/3 [==============================] - 0s 77ms/step - loss: 0.4345 - accuracy: 0.8026 - val_loss: 0.3600 - val_accuracy: 0.8632
Epoch 149/150
3/3 [==============================] - 0s 64ms/step - loss: 0.3778 - accuracy: 0.8127 - val_loss: 0.4573 - val_accuracy: 0.8211
1/3 [=========>....................] - ETA: 0s - loss: 0.4493 - accuracy: 0.7891Epoch 70/150
3/3 [==============================] - 0s 74ms/step - loss: 0.4433 - accuracy: 0.7974 - val_loss: 0.3601 - val_accuracy: 0.8632
Epoch 150/150
3/3 [==============================] - 0s 74ms/step - loss: 0.3529 - accuracy: 0.8522 - val_loss: 0.4577 - val_accuracy: 0.8211
Epoch 71/150
3/3 [==============================] - 0s 98ms/step - loss: 0.4113 - accuracy: 0.8000 - val_loss: 0.3601 - val_accuracy: 0.8632
3/3 [==============================] - 0s 85ms/step - loss: 0.3783 - accuracy: 0.8443 - val_loss: 0.4579 - val_accuracy: 0.8211
Epoch 72/150
3/3 [==============================] - 0s 53ms/step - loss: 0.3636 - accuracy: 0.8391 - val_loss: 0.4581 - val_accuracy: 0.8211
Epoch 73/150
3/3 [==============================] - 0s 39ms/step - loss: 0.3384 - accuracy: 0.8522 - val_loss: 0.4585 - val_accuracy: 0.8211
Epoch 74/150
3/3 [==============================] - 0s 44ms/step - loss: 0.3804 - accuracy: 0.8311 - val_loss: 0.4588 - val_accuracy: 0.8211
Epoch 75/150
3/3 [==============================] - 0s 47ms/step - loss: 0.3673 - accuracy: 0.8259 - val_loss: 0.4591 - val_accuracy: 0.8211
Epoch 76/150
3/3 [==============================] - 0s 56ms/step - loss: 0.3569 - accuracy: 0.8575 - val_loss: 0.4595 - val_accuracy: 0.8211
Epoch 77/150
3/3 [==============================] - 0s 47ms/step - loss: 0.3641 - accuracy: 0.8602 - val_loss: 0.4598 - val_accuracy: 0.8211
Epoch 78/150
3/3 [==============================] - 0s 38ms/step - loss: 0.3701 - accuracy: 0.8549 - val_loss: 0.4602 - val_accuracy: 0.8211
Epoch 79/150
3/3 [==============================] - 0s 39ms/step - loss: 0.3799 - accuracy: 0.8522 - val_loss: 0.4608 - val_accuracy: 0.8211
Epoch 80/150
3/3 [==============================] - 0s 41ms/step - loss: 0.3899 - accuracy: 0.8391 - val_loss: 0.4613 - val_accuracy: 0.8211
Epoch 81/150
3/3 [==============================] - 0s 52ms/step - loss: 0.3655 - accuracy: 0.8470 - val_loss: 0.4619 - val_accuracy: 0.8211
Epoch 82/150
3/3 [==============================] - 0s 48ms/step - loss: 0.3520 - accuracy: 0.8681 - val_loss: 0.4625 - val_accuracy: 0.8211
Epoch 83/150
3/3 [==============================] - 0s 48ms/step - loss: 0.3486 - accuracy: 0.8681 - val_loss: 0.4633 - val_accuracy: 0.8211
Epoch 84/150
3/3 [==============================] - 0s 37ms/step - loss: 0.3553 - accuracy: 0.8443 - val_loss: 0.4637 - val_accuracy: 0.8211
Epoch 85/150
3/3 [==============================] - 0s 38ms/step - loss: 0.3663 - accuracy: 0.8522 - val_loss: 0.4639 - val_accuracy: 0.8211
Epoch 86/150
3/3 [==============================] - 0s 36ms/step - loss: 0.3722 - accuracy: 0.8470 - val_loss: 0.4645 - val_accuracy: 0.8211
Epoch 87/150
3/3 [==============================] - 0s 36ms/step - loss: 0.3579 - accuracy: 0.8496 - val_loss: 0.4654 - val_accuracy: 0.8211
Epoch 88/150
3/3 [==============================] - 0s 36ms/step - loss: 0.3563 - accuracy: 0.8602 - val_loss: 0.4662 - val_accuracy: 0.8211
Epoch 89/150
3/3 [==============================] - 0s 38ms/step - loss: 0.3857 - accuracy: 0.8417 - val_loss: 0.4669 - val_accuracy: 0.8211
Epoch 90/150
3/3 [==============================] - 0s 40ms/step - loss: 0.3492 - accuracy: 0.8602 - val_loss: 0.4676 - val_accuracy: 0.8211
Epoch 91/150
3/3 [==============================] - 0s 39ms/step - loss: 0.3808 - accuracy: 0.8364 - val_loss: 0.4684 - val_accuracy: 0.8211
Epoch 92/150
3/3 [==============================] - 0s 38ms/step - loss: 0.3821 - accuracy: 0.8391 - val_loss: 0.4689 - val_accuracy: 0.8211
Epoch 93/150
3/3 [==============================] - 0s 37ms/step - loss: 0.3423 - accuracy: 0.8786 - val_loss: 0.4694 - val_accuracy: 0.8211
Epoch 94/150
3/3 [==============================] - 0s 39ms/step - loss: 0.3412 - accuracy: 0.8522 - val_loss: 0.4698 - val_accuracy: 0.8211
Epoch 95/150
3/3 [==============================] - 0s 46ms/step - loss: 0.3847 - accuracy: 0.8522 - val_loss: 0.4702 - val_accuracy: 0.8211
Epoch 96/150
3/3 [==============================] - 0s 44ms/step - loss: 0.3427 - accuracy: 0.8628 - val_loss: 0.4708 - val_accuracy: 0.8211
Epoch 97/150
3/3 [==============================] - 0s 39ms/step - loss: 0.3308 - accuracy: 0.8707 - val_loss: 0.4717 - val_accuracy: 0.8211
Epoch 98/150
3/3 [==============================] - 0s 40ms/step - loss: 0.3818 - accuracy: 0.8391 - val_loss: 0.4722 - val_accuracy: 0.8211
Epoch 99/150
3/3 [==============================] - 0s 44ms/step - loss: 0.3558 - accuracy: 0.8734 - val_loss: 0.4730 - val_accuracy: 0.8211
Epoch 100/150
3/3 [==============================] - 0s 35ms/step - loss: 0.3545 - accuracy: 0.8602 - val_loss: 0.4736 - val_accuracy: 0.8211
Epoch 101/150
3/3 [==============================] - 0s 51ms/step - loss: 0.3509 - accuracy: 0.8391 - val_loss: 0.4743 - val_accuracy: 0.8211
Epoch 102/150
3/3 [==============================] - 0s 52ms/step - loss: 0.3794 - accuracy: 0.8470 - val_loss: 0.4747 - val_accuracy: 0.8211
Epoch 103/150
3/3 [==============================] - 0s 51ms/step - loss: 0.3445 - accuracy: 0.8654 - val_loss: 0.4751 - val_accuracy: 0.8211
Epoch 104/150
3/3 [==============================] - 0s 42ms/step - loss: 0.3882 - accuracy: 0.8496 - val_loss: 0.4753 - val_accuracy: 0.8211
Epoch 105/150
3/3 [==============================] - 0s 45ms/step - loss: 0.3842 - accuracy: 0.8232 - val_loss: 0.4755 - val_accuracy: 0.8211
Epoch 106/150
3/3 [==============================] - 0s 41ms/step - loss: 0.3240 - accuracy: 0.8628 - val_loss: 0.4762 - val_accuracy: 0.8211
Epoch 107/150
3/3 [==============================] - 0s 43ms/step - loss: 0.3372 - accuracy: 0.8602 - val_loss: 0.4769 - val_accuracy: 0.8211
Epoch 108/150
3/3 [==============================] - 0s 47ms/step - loss: 0.3397 - accuracy: 0.8470 - val_loss: 0.4774 - val_accuracy: 0.8211
Epoch 109/150
3/3 [==============================] - 0s 42ms/step - loss: 0.3219 - accuracy: 0.8628 - val_loss: 0.4779 - val_accuracy: 0.8211
Epoch 110/150
3/3 [==============================] - 0s 34ms/step - loss: 0.3358 - accuracy: 0.8575 - val_loss: 0.4783 - val_accuracy: 0.8211
Epoch 111/150
3/3 [==============================] - 0s 33ms/step - loss: 0.3365 - accuracy: 0.8839 - val_loss: 0.4789 - val_accuracy: 0.8211
Epoch 112/150
3/3 [==============================] - 0s 38ms/step - loss: 0.3185 - accuracy: 0.8549 - val_loss: 0.4793 - val_accuracy: 0.8211
Epoch 113/150
3/3 [==============================] - 0s 38ms/step - loss: 0.3624 - accuracy: 0.8654 - val_loss: 0.4801 - val_accuracy: 0.8211
Epoch 114/150
3/3 [==============================] - 0s 38ms/step - loss: 0.3727 - accuracy: 0.8496 - val_loss: 0.4805 - val_accuracy: 0.8211
Epoch 115/150
3/3 [==============================] - 0s 40ms/step - loss: 0.3484 - accuracy: 0.8681 - val_loss: 0.4809 - val_accuracy: 0.8211
Epoch 116/150
3/3 [==============================] - 0s 36ms/step - loss: 0.3619 - accuracy: 0.8628 - val_loss: 0.4813 - val_accuracy: 0.8211
Epoch 117/150
3/3 [==============================] - 0s 37ms/step - loss: 0.3593 - accuracy: 0.8549 - val_loss: 0.4816 - val_accuracy: 0.8211
Epoch 118/150
3/3 [==============================] - 0s 34ms/step - loss: 0.3308 - accuracy: 0.8707 - val_loss: 0.4822 - val_accuracy: 0.8211
Epoch 119/150
3/3 [==============================] - 0s 42ms/step - loss: 0.3368 - accuracy: 0.8602 - val_loss: 0.4831 - val_accuracy: 0.8211
Epoch 120/150
3/3 [==============================] - 0s 37ms/step - loss: 0.3715 - accuracy: 0.8470 - val_loss: 0.4835 - val_accuracy: 0.8211
Epoch 121/150
3/3 [==============================] - 0s 44ms/step - loss: 0.3488 - accuracy: 0.8654 - val_loss: 0.4839 - val_accuracy: 0.8211
Epoch 122/150
3/3 [==============================] - 0s 36ms/step - loss: 0.3640 - accuracy: 0.8575 - val_loss: 0.4843 - val_accuracy: 0.8211
Epoch 123/150
3/3 [==============================] - 0s 40ms/step - loss: 0.3537 - accuracy: 0.8470 - val_loss: 0.4848 - val_accuracy: 0.8211
Epoch 124/150
3/3 [==============================] - 0s 29ms/step - loss: 0.3500 - accuracy: 0.8470 - val_loss: 0.4853 - val_accuracy: 0.8211
Epoch 125/150
3/3 [==============================] - 0s 37ms/step - loss: 0.3477 - accuracy: 0.8522 - val_loss: 0.4860 - val_accuracy: 0.8211
Epoch 126/150
3/3 [==============================] - 0s 41ms/step - loss: 0.3464 - accuracy: 0.8575 - val_loss: 0.4869 - val_accuracy: 0.8211
Epoch 127/150
3/3 [==============================] - 0s 42ms/step - loss: 0.3200 - accuracy: 0.8654 - val_loss: 0.4879 - val_accuracy: 0.8211
Epoch 128/150
3/3 [==============================] - 0s 37ms/step - loss: 0.3200 - accuracy: 0.8602 - val_loss: 0.4883 - val_accuracy: 0.8211
Epoch 129/150
3/3 [==============================] - 0s 38ms/step - loss: 0.3182 - accuracy: 0.8628 - val_loss: 0.4887 - val_accuracy: 0.8211
Epoch 130/150
3/3 [==============================] - 0s 36ms/step - loss: 0.3178 - accuracy: 0.8734 - val_loss: 0.4894 - val_accuracy: 0.8105
Epoch 131/150
3/3 [==============================] - 0s 49ms/step - loss: 0.3337 - accuracy: 0.8707 - val_loss: 0.4896 - val_accuracy: 0.8105
Epoch 132/150
3/3 [==============================] - 0s 44ms/step - loss: 0.3518 - accuracy: 0.8522 - val_loss: 0.4902 - val_accuracy: 0.8105
Epoch 133/150
3/3 [==============================] - 0s 53ms/step - loss: 0.3336 - accuracy: 0.8602 - val_loss: 0.4907 - val_accuracy: 0.8105
Epoch 134/150
3/3 [==============================] - 0s 59ms/step - loss: 0.3117 - accuracy: 0.8865 - val_loss: 0.4908 - val_accuracy: 0.8105
Epoch 135/150
3/3 [==============================] - 0s 54ms/step - loss: 0.3532 - accuracy: 0.8602 - val_loss: 0.4909 - val_accuracy: 0.8105
Epoch 136/150
3/3 [==============================] - 0s 50ms/step - loss: 0.3400 - accuracy: 0.8575 - val_loss: 0.4916 - val_accuracy: 0.8105
Epoch 137/150
3/3 [==============================] - 0s 40ms/step - loss: 0.3250 - accuracy: 0.8734 - val_loss: 0.4918 - val_accuracy: 0.8105
Epoch 138/150
3/3 [==============================] - 0s 36ms/step - loss: 0.3548 - accuracy: 0.8654 - val_loss: 0.4926 - val_accuracy: 0.8105
Epoch 139/150
3/3 [==============================] - 0s 43ms/step - loss: 0.3235 - accuracy: 0.8654 - val_loss: 0.4931 - val_accuracy: 0.8105
Epoch 140/150
3/3 [==============================] - 0s 40ms/step - loss: 0.3349 - accuracy: 0.8654 - val_loss: 0.4939 - val_accuracy: 0.8105
Epoch 141/150
3/3 [==============================] - 0s 40ms/step - loss: 0.3379 - accuracy: 0.8654 - val_loss: 0.4945 - val_accuracy: 0.8105
Epoch 142/150
3/3 [==============================] - 0s 36ms/step - loss: 0.3452 - accuracy: 0.8681 - val_loss: 0.4953 - val_accuracy: 0.8105
Epoch 143/150
3/3 [==============================] - 0s 33ms/step - loss: 0.3459 - accuracy: 0.8549 - val_loss: 0.4963 - val_accuracy: 0.8105
Epoch 144/150
3/3 [==============================] - 0s 40ms/step - loss: 0.3665 - accuracy: 0.8734 - val_loss: 0.4972 - val_accuracy: 0.8105
Epoch 145/150
3/3 [==============================] - 0s 43ms/step - loss: 0.3389 - accuracy: 0.8496 - val_loss: 0.4982 - val_accuracy: 0.8105
Epoch 146/150
3/3 [==============================] - 0s 36ms/step - loss: 0.3362 - accuracy: 0.8681 - val_loss: 0.4990 - val_accuracy: 0.8105
Epoch 147/150
3/3 [==============================] - 0s 38ms/step - loss: 0.3496 - accuracy: 0.8602 - val_loss: 0.5002 - val_accuracy: 0.8105
Epoch 148/150
3/3 [==============================] - 0s 40ms/step - loss: 0.3440 - accuracy: 0.8654 - val_loss: 0.5013 - val_accuracy: 0.8105
Epoch 149/150
3/3 [==============================] - 0s 32ms/step - loss: 0.3342 - accuracy: 0.8628 - val_loss: 0.5020 - val_accuracy: 0.8105
Epoch 150/150
3/3 [==============================] - 0s 40ms/step - loss: 0.3393 - accuracy: 0.8707 - val_loss: 0.5027 - val_accuracy: 0.8105
2/2 [==============================] - 0s 7ms/step - loss: 0.5676 - accuracy: 0.7983
Epoch 1/150
3/3 [==============================] - 2s 222ms/step - loss: 0.8235 - accuracy: 0.5184 - val_loss: 0.6309 - val_accuracy: 0.6842
Epoch 2/150
3/3 [==============================] - 0s 35ms/step - loss: 0.7281 - accuracy: 0.6026 - val_loss: 0.6151 - val_accuracy: 0.7053
Epoch 3/150
3/3 [==============================] - 0s 38ms/step - loss: 0.7506 - accuracy: 0.5974 - val_loss: 0.6024 - val_accuracy: 0.7368
Epoch 4/150
3/3 [==============================] - 0s 42ms/step - loss: 0.6299 - accuracy: 0.6658 - val_loss: 0.5925 - val_accuracy: 0.7368
Epoch 5/150
3/3 [==============================] - 0s 36ms/step - loss: 0.6647 - accuracy: 0.6816 - val_loss: 0.5845 - val_accuracy: 0.7579
Epoch 6/150
3/3 [==============================] - 0s 39ms/step - loss: 0.6581 - accuracy: 0.6763 - val_loss: 0.5779 - val_accuracy: 0.7579
Epoch 7/150
3/3 [==============================] - 0s 36ms/step - loss: 0.6047 - accuracy: 0.7132 - val_loss: 0.5724 - val_accuracy: 0.7579
Epoch 8/150
3/3 [==============================] - 0s 39ms/step - loss: 0.5905 - accuracy: 0.7395 - val_loss: 0.5678 - val_accuracy: 0.7368
Epoch 9/150
3/3 [==============================] - 0s 40ms/step - loss: 0.5591 - accuracy: 0.7447 - val_loss: 0.5644 - val_accuracy: 0.7474
Epoch 10/150
3/3 [==============================] - 0s 39ms/step - loss: 0.5554 - accuracy: 0.7447 - val_loss: 0.5620 - val_accuracy: 0.7579
Epoch 11/150
3/3 [==============================] - 0s 48ms/step - loss: 0.5357 - accuracy: 0.7605 - val_loss: 0.5601 - val_accuracy: 0.7684
Epoch 12/150
3/3 [==============================] - 0s 62ms/step - loss: 0.5590 - accuracy: 0.7395 - val_loss: 0.5587 - val_accuracy: 0.7789
Epoch 13/150
3/3 [==============================] - 0s 52ms/step - loss: 0.5128 - accuracy: 0.7605 - val_loss: 0.5574 - val_accuracy: 0.7684
Epoch 14/150
3/3 [==============================] - 0s 61ms/step - loss: 0.5261 - accuracy: 0.7605 - val_loss: 0.5567 - val_accuracy: 0.7579
Epoch 15/150
3/3 [==============================] - 0s 56ms/step - loss: 0.5675 - accuracy: 0.7342 - val_loss: 0.5555 - val_accuracy: 0.7368
Epoch 16/150
3/3 [==============================] - 0s 46ms/step - loss: 0.5272 - accuracy: 0.7368 - val_loss: 0.5546 - val_accuracy: 0.7368
Epoch 17/150
3/3 [==============================] - 0s 51ms/step - loss: 0.5441 - accuracy: 0.7368 - val_loss: 0.5540 - val_accuracy: 0.7474
Epoch 18/150
3/3 [==============================] - 0s 47ms/step - loss: 0.5255 - accuracy: 0.7605 - val_loss: 0.5534 - val_accuracy: 0.7579
Epoch 19/150
3/3 [==============================] - 0s 42ms/step - loss: 0.5343 - accuracy: 0.7553 - val_loss: 0.5532 - val_accuracy: 0.7579
Epoch 20/150
3/3 [==============================] - 0s 53ms/step - loss: 0.5331 - accuracy: 0.7474 - val_loss: 0.5529 - val_accuracy: 0.7579
Epoch 21/150
3/3 [==============================] - 0s 66ms/step - loss: 0.5347 - accuracy: 0.7553 - val_loss: 0.5527 - val_accuracy: 0.7474
Epoch 22/150
3/3 [==============================] - 0s 54ms/step - loss: 0.5158 - accuracy: 0.7684 - val_loss: 0.5522 - val_accuracy: 0.7474
Epoch 23/150
3/3 [==============================] - 0s 56ms/step - loss: 0.5112 - accuracy: 0.7632 - val_loss: 0.5521 - val_accuracy: 0.7474
Epoch 24/150
3/3 [==============================] - 0s 61ms/step - loss: 0.5015 - accuracy: 0.7632 - val_loss: 0.5520 - val_accuracy: 0.7474
Epoch 25/150
3/3 [==============================] - 0s 52ms/step - loss: 0.4495 - accuracy: 0.8079 - val_loss: 0.5515 - val_accuracy: 0.7474
Epoch 26/150
3/3 [==============================] - 0s 48ms/step - loss: 0.5112 - accuracy: 0.7579 - val_loss: 0.5500 - val_accuracy: 0.7474
Epoch 27/150
3/3 [==============================] - 0s 60ms/step - loss: 0.5259 - accuracy: 0.7658 - val_loss: 0.5493 - val_accuracy: 0.7368
Epoch 28/150
3/3 [==============================] - 0s 51ms/step - loss: 0.4930 - accuracy: 0.7842 - val_loss: 0.5485 - val_accuracy: 0.7368
Epoch 29/150
3/3 [==============================] - 0s 49ms/step - loss: 0.5044 - accuracy: 0.7789 - val_loss: 0.5479 - val_accuracy: 0.7368
Epoch 30/150
3/3 [==============================] - 0s 54ms/step - loss: 0.4984 - accuracy: 0.7711 - val_loss: 0.5467 - val_accuracy: 0.7368
Epoch 31/150
3/3 [==============================] - 0s 52ms/step - loss: 0.4862 - accuracy: 0.7947 - val_loss: 0.5459 - val_accuracy: 0.7474
Epoch 32/150
3/3 [==============================] - 0s 51ms/step - loss: 0.4505 - accuracy: 0.8000 - val_loss: 0.5445 - val_accuracy: 0.7474
Epoch 33/150
3/3 [==============================] - 0s 48ms/step - loss: 0.4829 - accuracy: 0.7947 - val_loss: 0.5435 - val_accuracy: 0.7474
Epoch 34/150
3/3 [==============================] - 0s 48ms/step - loss: 0.4758 - accuracy: 0.8184 - val_loss: 0.5426 - val_accuracy: 0.7579
Epoch 35/150
3/3 [==============================] - 0s 46ms/step - loss: 0.4826 - accuracy: 0.7763 - val_loss: 0.5415 - val_accuracy: 0.7579
Epoch 36/150
3/3 [==============================] - 0s 48ms/step - loss: 0.4910 - accuracy: 0.7895 - val_loss: 0.5399 - val_accuracy: 0.7579
Epoch 37/150
3/3 [==============================] - 0s 48ms/step - loss: 0.4902 - accuracy: 0.7842 - val_loss: 0.5385 - val_accuracy: 0.7579
Epoch 38/150
3/3 [==============================] - 0s 47ms/step - loss: 0.4600 - accuracy: 0.7868 - val_loss: 0.5368 - val_accuracy: 0.7579
Epoch 39/150
3/3 [==============================] - 0s 53ms/step - loss: 0.4482 - accuracy: 0.8105 - val_loss: 0.5351 - val_accuracy: 0.7684
Epoch 40/150
3/3 [==============================] - 0s 50ms/step - loss: 0.4883 - accuracy: 0.7895 - val_loss: 0.5338 - val_accuracy: 0.7579
Epoch 41/150
3/3 [==============================] - 0s 55ms/step - loss: 0.4731 - accuracy: 0.7921 - val_loss: 0.5318 - val_accuracy: 0.7579
Epoch 42/150
3/3 [==============================] - 0s 45ms/step - loss: 0.4632 - accuracy: 0.8026 - val_loss: 0.5303 - val_accuracy: 0.7579
Epoch 43/150
3/3 [==============================] - 0s 42ms/step - loss: 0.4798 - accuracy: 0.7711 - val_loss: 0.5284 - val_accuracy: 0.7579
Epoch 44/150
3/3 [==============================] - 0s 45ms/step - loss: 0.4609 - accuracy: 0.8132 - val_loss: 0.5266 - val_accuracy: 0.7579
Epoch 45/150
3/3 [==============================] - 0s 51ms/step - loss: 0.4639 - accuracy: 0.7868 - val_loss: 0.5251 - val_accuracy: 0.7579
Epoch 46/150
3/3 [==============================] - 0s 45ms/step - loss: 0.4417 - accuracy: 0.8132 - val_loss: 0.5243 - val_accuracy: 0.7684
Epoch 47/150
3/3 [==============================] - 0s 53ms/step - loss: 0.4447 - accuracy: 0.8105 - val_loss: 0.5231 - val_accuracy: 0.7684
Epoch 48/150
3/3 [==============================] - 0s 54ms/step - loss: 0.4708 - accuracy: 0.8132 - val_loss: 0.5218 - val_accuracy: 0.7895
Epoch 49/150
3/3 [==============================] - 0s 59ms/step - loss: 0.4439 - accuracy: 0.8211 - val_loss: 0.5208 - val_accuracy: 0.7895
Epoch 50/150
3/3 [==============================] - 0s 48ms/step - loss: 0.4715 - accuracy: 0.7842 - val_loss: 0.5197 - val_accuracy: 0.7895
Epoch 51/150
3/3 [==============================] - 0s 47ms/step - loss: 0.4720 - accuracy: 0.8053 - val_loss: 0.5187 - val_accuracy: 0.7895
Epoch 52/150
3/3 [==============================] - 0s 60ms/step - loss: 0.4635 - accuracy: 0.8158 - val_loss: 0.5182 - val_accuracy: 0.8000
Epoch 53/150
3/3 [==============================] - 0s 44ms/step - loss: 0.4060 - accuracy: 0.8158 - val_loss: 0.5176 - val_accuracy: 0.8000
Epoch 54/150
3/3 [==============================] - 0s 44ms/step - loss: 0.4823 - accuracy: 0.7974 - val_loss: 0.5170 - val_accuracy: 0.8000
Epoch 55/150
3/3 [==============================] - 0s 44ms/step - loss: 0.4574 - accuracy: 0.8000 - val_loss: 0.5159 - val_accuracy: 0.8000
Epoch 56/150
3/3 [==============================] - 0s 42ms/step - loss: 0.4701 - accuracy: 0.8105 - val_loss: 0.5150 - val_accuracy: 0.8000
Epoch 57/150
3/3 [==============================] - 0s 58ms/step - loss: 0.4510 - accuracy: 0.8000 - val_loss: 0.5130 - val_accuracy: 0.8000
Epoch 58/150
3/3 [==============================] - 0s 61ms/step - loss: 0.4557 - accuracy: 0.7974 - val_loss: 0.5118 - val_accuracy: 0.8000
Epoch 59/150
3/3 [==============================] - 0s 77ms/step - loss: 0.4657 - accuracy: 0.7947 - val_loss: 0.5109 - val_accuracy: 0.8000
Epoch 60/150
3/3 [==============================] - 0s 67ms/step - loss: 0.4333 - accuracy: 0.8000 - val_loss: 0.5095 - val_accuracy: 0.8105
Epoch 61/150
2/2 [==============================] - 0s 27ms/step - loss: 0.4247 - accuracy: 0.8186
3/3 [==============================] - 0s 67ms/step - loss: 0.4610 - accuracy: 0.7947 - val_loss: 0.5083 - val_accuracy: 0.8105
Epoch 62/150
3/3 [==============================] - 0s 78ms/step - loss: 0.4415 - accuracy: 0.7974 - val_loss: 0.5069 - val_accuracy: 0.8105
Epoch 63/150
3/3 [==============================] - 0s 73ms/step - loss: 0.4501 - accuracy: 0.8316 - val_loss: 0.5058 - val_accuracy: 0.8105
Epoch 64/150
3/3 [==============================] - 0s 69ms/step - loss: 0.4499 - accuracy: 0.8211 - val_loss: 0.5044 - val_accuracy: 0.8105
Epoch 65/150
1/3 [=========>....................] - ETA: 0s - loss: 0.4354 - accuracy: 0.8125Epoch 1/150
3/3 [==============================] - 0s 58ms/step - loss: 0.4714 - accuracy: 0.7763 - val_loss: 0.5029 - val_accuracy: 0.8105
Epoch 66/150
3/3 [==============================] - 0s 64ms/step - loss: 0.4396 - accuracy: 0.7921 - val_loss: 0.5009 - val_accuracy: 0.8105
Epoch 67/150
3/3 [==============================] - 0s 63ms/step - loss: 0.4521 - accuracy: 0.8026 - val_loss: 0.4996 - val_accuracy: 0.8105
Epoch 68/150
3/3 [==============================] - 0s 70ms/step - loss: 0.4393 - accuracy: 0.8079 - val_loss: 0.4980 - val_accuracy: 0.8105
Epoch 69/150
3/3 [==============================] - 0s 75ms/step - loss: 0.4675 - accuracy: 0.7868 - val_loss: 0.4966 - val_accuracy: 0.8105
Epoch 70/150
3/3 [==============================] - 0s 68ms/step - loss: 0.4588 - accuracy: 0.8237 - val_loss: 0.4951 - val_accuracy: 0.8000
Epoch 71/150
3/3 [==============================] - 0s 68ms/step - loss: 0.4315 - accuracy: 0.8237 - val_loss: 0.4941 - val_accuracy: 0.8000
Epoch 72/150
3/3 [==============================] - 0s 67ms/step - loss: 0.4628 - accuracy: 0.8000 - val_loss: 0.4926 - val_accuracy: 0.8000
Epoch 73/150
3/3 [==============================] - 0s 68ms/step - loss: 0.4067 - accuracy: 0.8184 - val_loss: 0.4912 - val_accuracy: 0.8000
Epoch 74/150
3/3 [==============================] - 0s 69ms/step - loss: 0.4299 - accuracy: 0.8263 - val_loss: 0.4898 - val_accuracy: 0.8000
Epoch 75/150
3/3 [==============================] - 0s 67ms/step - loss: 0.4090 - accuracy: 0.8263 - val_loss: 0.4891 - val_accuracy: 0.8000
Epoch 76/150
3/3 [==============================] - 0s 70ms/step - loss: 0.4512 - accuracy: 0.8105 - val_loss: 0.4885 - val_accuracy: 0.8000
Epoch 77/150
3/3 [==============================] - 0s 74ms/step - loss: 0.4367 - accuracy: 0.8053 - val_loss: 0.4875 - val_accuracy: 0.8000
Epoch 78/150
3/3 [==============================] - 0s 66ms/step - loss: 0.4571 - accuracy: 0.7947 - val_loss: 0.4867 - val_accuracy: 0.8000
Epoch 79/150
3/3 [==============================] - 0s 66ms/step - loss: 0.4482 - accuracy: 0.8053 - val_loss: 0.4859 - val_accuracy: 0.8105
Epoch 80/150
3/3 [==============================] - 0s 61ms/step - loss: 0.4409 - accuracy: 0.8105 - val_loss: 0.4851 - val_accuracy: 0.8105
Epoch 81/150
3/3 [==============================] - 0s 69ms/step - loss: 0.3814 - accuracy: 0.8316 - val_loss: 0.4851 - val_accuracy: 0.8105
Epoch 82/150
3/3 [==============================] - 0s 71ms/step - loss: 0.4320 - accuracy: 0.8079 - val_loss: 0.4845 - val_accuracy: 0.8105
Epoch 83/150
3/3 [==============================] - 0s 71ms/step - loss: 0.4511 - accuracy: 0.8184 - val_loss: 0.4839 - val_accuracy: 0.8105
Epoch 84/150
3/3 [==============================] - 0s 74ms/step - loss: 0.4384 - accuracy: 0.8132 - val_loss: 0.4835 - val_accuracy: 0.8105
Epoch 85/150
3/3 [==============================] - 0s 67ms/step - loss: 0.4200 - accuracy: 0.8289 - val_loss: 0.4831 - val_accuracy: 0.8105
Epoch 86/150
3/3 [==============================] - 0s 67ms/step - loss: 0.4014 - accuracy: 0.8500 - val_loss: 0.4825 - val_accuracy: 0.8105
Epoch 87/150
3/3 [==============================] - 0s 70ms/step - loss: 0.4306 - accuracy: 0.8211 - val_loss: 0.4820 - val_accuracy: 0.8105
Epoch 88/150
3/3 [==============================] - 0s 75ms/step - loss: 0.4317 - accuracy: 0.8237 - val_loss: 0.4818 - val_accuracy: 0.8105
Epoch 89/150
3/3 [==============================] - 0s 70ms/step - loss: 0.4153 - accuracy: 0.8368 - val_loss: 0.4810 - val_accuracy: 0.8105
Epoch 90/150
3/3 [==============================] - 0s 71ms/step - loss: 0.3862 - accuracy: 0.8237 - val_loss: 0.4807 - val_accuracy: 0.8105
Epoch 91/150
3/3 [==============================] - 0s 69ms/step - loss: 0.4573 - accuracy: 0.8237 - val_loss: 0.4800 - val_accuracy: 0.8105
Epoch 92/150
3/3 [==============================] - 0s 64ms/step - loss: 0.3959 - accuracy: 0.8342 - val_loss: 0.4793 - val_accuracy: 0.8105
Epoch 93/150
3/3 [==============================] - 0s 73ms/step - loss: 0.4164 - accuracy: 0.8237 - val_loss: 0.4788 - val_accuracy: 0.8105
Epoch 94/150
3/3 [==============================] - 0s 73ms/step - loss: 0.4603 - accuracy: 0.8000 - val_loss: 0.4782 - val_accuracy: 0.8105
Epoch 95/150
3/3 [==============================] - 0s 73ms/step - loss: 0.4280 - accuracy: 0.8053 - val_loss: 0.4772 - val_accuracy: 0.8105
Epoch 96/150
3/3 [==============================] - 0s 68ms/step - loss: 0.4271 - accuracy: 0.8158 - val_loss: 0.4771 - val_accuracy: 0.8105
Epoch 97/150
3/3 [==============================] - 0s 68ms/step - loss: 0.4225 - accuracy: 0.8132 - val_loss: 0.4765 - val_accuracy: 0.8105
Epoch 98/150
3/3 [==============================] - 0s 66ms/step - loss: 0.4519 - accuracy: 0.8000 - val_loss: 0.4762 - val_accuracy: 0.8105
Epoch 99/150
3/3 [==============================] - 0s 62ms/step - loss: 0.4257 - accuracy: 0.8079 - val_loss: 0.4755 - val_accuracy: 0.8105
Epoch 100/150
3/3 [==============================] - 0s 59ms/step - loss: 0.4049 - accuracy: 0.8368 - val_loss: 0.4752 - val_accuracy: 0.8105
Epoch 101/150
3/3 [==============================] - 6s 540ms/step - loss: 0.8878 - accuracy: 0.4737 - val_loss: 0.7005 - val_accuracy: 0.4842
Epoch 2/150
3/3 [==============================] - 0s 83ms/step - loss: 0.4269 - accuracy: 0.8105 - val_loss: 0.4748 - val_accuracy: 0.8105
Epoch 102/150
3/3 [==============================] - 0s 75ms/step - loss: 0.7958 - accuracy: 0.5289 - val_loss: 0.6728 - val_accuracy: 0.6105
Epoch 3/150
3/3 [==============================] - 0s 72ms/step - loss: 0.4347 - accuracy: 0.8368 - val_loss: 0.4742 - val_accuracy: 0.8105
Epoch 103/150
3/3 [==============================] - 0s 76ms/step - loss: 0.7865 - accuracy: 0.5474 - val_loss: 0.6508 - val_accuracy: 0.7053
Epoch 4/150
3/3 [==============================] - 0s 81ms/step - loss: 0.3835 - accuracy: 0.8447 - val_loss: 0.4740 - val_accuracy: 0.8105
Epoch 104/150
3/3 [==============================] - 0s 87ms/step - loss: 0.6862 - accuracy: 0.6421 - val_loss: 0.6319 - val_accuracy: 0.6842
Epoch 5/150
3/3 [==============================] - 0s 60ms/step - loss: 0.4174 - accuracy: 0.8211 - val_loss: 0.4735 - val_accuracy: 0.8105
Epoch 105/150
3/3 [==============================] - 0s 72ms/step - loss: 0.6040 - accuracy: 0.7079 - val_loss: 0.6161 - val_accuracy: 0.7158
Epoch 6/150
3/3 [==============================] - 0s 76ms/step - loss: 0.3990 - accuracy: 0.8447 - val_loss: 0.4729 - val_accuracy: 0.8105
Epoch 106/150
3/3 [==============================] - 0s 59ms/step - loss: 0.6538 - accuracy: 0.6895 - val_loss: 0.6023 - val_accuracy: 0.7579
Epoch 7/150
3/3 [==============================] - 0s 66ms/step - loss: 0.3984 - accuracy: 0.8447 - val_loss: 0.4729 - val_accuracy: 0.8105
Epoch 107/150
3/3 [==============================] - 0s 83ms/step - loss: 0.6075 - accuracy: 0.6763 - val_loss: 0.5906 - val_accuracy: 0.8000
Epoch 8/150
3/3 [==============================] - 0s 79ms/step - loss: 0.4197 - accuracy: 0.8237 - val_loss: 0.4731 - val_accuracy: 0.8105
Epoch 108/150
3/3 [==============================] - 0s 84ms/step - loss: 0.5534 - accuracy: 0.7132 - val_loss: 0.5812 - val_accuracy: 0.8105
Epoch 9/150
3/3 [==============================] - 0s 86ms/step - loss: 0.4152 - accuracy: 0.8237 - val_loss: 0.4732 - val_accuracy: 0.8000
Epoch 109/150
3/3 [==============================] - 0s 89ms/step - loss: 0.5647 - accuracy: 0.7500 - val_loss: 0.5721 - val_accuracy: 0.8000
Epoch 10/150
3/3 [==============================] - 0s 73ms/step - loss: 0.4516 - accuracy: 0.8079 - val_loss: 0.4731 - val_accuracy: 0.8000
Epoch 110/150
3/3 [==============================] - 0s 81ms/step - loss: 0.5742 - accuracy: 0.7211 - val_loss: 0.5638 - val_accuracy: 0.8000
Epoch 11/150
3/3 [==============================] - 0s 84ms/step - loss: 0.4433 - accuracy: 0.8053 - val_loss: 0.4735 - val_accuracy: 0.8000
Epoch 111/150
3/3 [==============================] - 0s 84ms/step - loss: 0.5586 - accuracy: 0.7316 - val_loss: 0.5566 - val_accuracy: 0.8105
Epoch 12/150
3/3 [==============================] - 0s 98ms/step - loss: 0.3752 - accuracy: 0.8421 - val_loss: 0.4738 - val_accuracy: 0.8000
Epoch 112/150
3/3 [==============================] - 0s 78ms/step - loss: 0.5463 - accuracy: 0.7447 - val_loss: 0.5500 - val_accuracy: 0.8211
Epoch 13/150
3/3 [==============================] - 0s 54ms/step - loss: 0.4271 - accuracy: 0.8237 - val_loss: 0.4740 - val_accuracy: 0.8000
Epoch 113/150
3/3 [==============================] - 0s 68ms/step - loss: 0.5146 - accuracy: 0.7421 - val_loss: 0.5439 - val_accuracy: 0.8316
Epoch 14/150
3/3 [==============================] - 0s 84ms/step - loss: 0.3968 - accuracy: 0.8316 - val_loss: 0.4737 - val_accuracy: 0.8000
Epoch 114/150
3/3 [==============================] - 0s 81ms/step - loss: 0.5455 - accuracy: 0.7316 - val_loss: 0.5379 - val_accuracy: 0.8316
Epoch 15/150
3/3 [==============================] - 0s 90ms/step - loss: 0.4401 - accuracy: 0.8158 - val_loss: 0.4737 - val_accuracy: 0.8000
Epoch 115/150
3/3 [==============================] - 0s 88ms/step - loss: 0.5683 - accuracy: 0.7395 - val_loss: 0.5323 - val_accuracy: 0.8316
Epoch 16/150
3/3 [==============================] - 0s 73ms/step - loss: 0.3977 - accuracy: 0.8421 - val_loss: 0.4740 - val_accuracy: 0.8000
Epoch 116/150
3/3 [==============================] - 0s 75ms/step - loss: 0.5116 - accuracy: 0.7789 - val_loss: 0.5267 - val_accuracy: 0.8316
Epoch 17/150
3/3 [==============================] - 0s 72ms/step - loss: 0.4455 - accuracy: 0.8132 - val_loss: 0.4740 - val_accuracy: 0.8000
Epoch 117/150
3/3 [==============================] - 0s 73ms/step - loss: 0.4787 - accuracy: 0.7684 - val_loss: 0.5212 - val_accuracy: 0.8526
Epoch 18/150
3/3 [==============================] - 0s 72ms/step - loss: 0.4242 - accuracy: 0.8132 - val_loss: 0.4740 - val_accuracy: 0.7895
Epoch 118/150
3/3 [==============================] - 0s 68ms/step - loss: 0.5293 - accuracy: 0.7684 - val_loss: 0.5169 - val_accuracy: 0.8526
Epoch 19/150
3/3 [==============================] - 0s 64ms/step - loss: 0.3984 - accuracy: 0.8263 - val_loss: 0.4739 - val_accuracy: 0.7895
Epoch 119/150
3/3 [==============================] - 0s 62ms/step - loss: 0.5076 - accuracy: 0.7711 - val_loss: 0.5121 - val_accuracy: 0.8526
Epoch 20/150
3/3 [==============================] - 0s 64ms/step - loss: 0.4334 - accuracy: 0.8158 - val_loss: 0.4736 - val_accuracy: 0.7895
Epoch 120/150
3/3 [==============================] - 0s 63ms/step - loss: 0.5154 - accuracy: 0.7632 - val_loss: 0.5077 - val_accuracy: 0.8526
Epoch 21/150
3/3 [==============================] - 0s 64ms/step - loss: 0.4134 - accuracy: 0.8237 - val_loss: 0.4736 - val_accuracy: 0.7895
Epoch 121/150
3/3 [==============================] - 0s 74ms/step - loss: 0.5203 - accuracy: 0.7368 - val_loss: 0.5035 - val_accuracy: 0.8737
Epoch 22/150
3/3 [==============================] - 0s 73ms/step - loss: 0.4398 - accuracy: 0.8079 - val_loss: 0.4735 - val_accuracy: 0.7895
Epoch 122/150
3/3 [==============================] - 0s 66ms/step - loss: 0.5105 - accuracy: 0.7632 - val_loss: 0.4996 - val_accuracy: 0.8737
Epoch 23/150
3/3 [==============================] - 0s 66ms/step - loss: 0.3930 - accuracy: 0.8316 - val_loss: 0.4733 - val_accuracy: 0.7895
Epoch 123/150
3/3 [==============================] - 0s 78ms/step - loss: 0.4897 - accuracy: 0.7684 - val_loss: 0.4959 - val_accuracy: 0.8737
Epoch 24/150
3/3 [==============================] - 0s 64ms/step - loss: 0.4171 - accuracy: 0.8289 - val_loss: 0.4733 - val_accuracy: 0.7895
Epoch 124/150
3/3 [==============================] - 0s 63ms/step - loss: 0.4902 - accuracy: 0.7711 - val_loss: 0.4929 - val_accuracy: 0.8632
Epoch 25/150
3/3 [==============================] - 0s 54ms/step - loss: 0.4372 - accuracy: 0.8132 - val_loss: 0.4732 - val_accuracy: 0.8000
Epoch 125/150
3/3 [==============================] - 0s 70ms/step - loss: 0.4823 - accuracy: 0.7816 - val_loss: 0.4900 - val_accuracy: 0.8632
Epoch 26/150
3/3 [==============================] - 0s 79ms/step - loss: 0.4228 - accuracy: 0.8211 - val_loss: 0.4735 - val_accuracy: 0.8000
Epoch 126/150
3/3 [==============================] - 0s 91ms/step - loss: 0.5017 - accuracy: 0.7632 - val_loss: 0.4869 - val_accuracy: 0.8632
Epoch 27/150
3/3 [==============================] - 0s 65ms/step - loss: 0.3950 - accuracy: 0.8421 - val_loss: 0.4736 - val_accuracy: 0.8000
Epoch 127/150
3/3 [==============================] - 0s 80ms/step - loss: 0.4537 - accuracy: 0.7868 - val_loss: 0.4837 - val_accuracy: 0.8632
Epoch 28/150
3/3 [==============================] - 0s 90ms/step - loss: 0.4124 - accuracy: 0.8395 - val_loss: 0.4743 - val_accuracy: 0.8000
Epoch 128/150
3/3 [==============================] - 0s 79ms/step - loss: 0.4841 - accuracy: 0.7763 - val_loss: 0.4805 - val_accuracy: 0.8632
Epoch 29/150
3/3 [==============================] - 0s 82ms/step - loss: 0.4009 - accuracy: 0.8421 - val_loss: 0.4743 - val_accuracy: 0.8000
Epoch 129/150
3/3 [==============================] - 0s 93ms/step - loss: 0.4724 - accuracy: 0.7763 - val_loss: 0.4774 - val_accuracy: 0.8632
Epoch 30/150
3/3 [==============================] - 0s 94ms/step - loss: 0.4002 - accuracy: 0.8500 - val_loss: 0.4746 - val_accuracy: 0.8000
Epoch 130/150
3/3 [==============================] - 0s 75ms/step - loss: 0.4919 - accuracy: 0.7684 - val_loss: 0.4742 - val_accuracy: 0.8737
Epoch 31/150
3/3 [==============================] - 0s 97ms/step - loss: 0.3955 - accuracy: 0.8500 - val_loss: 0.4749 - val_accuracy: 0.8000
Epoch 131/150
3/3 [==============================] - 0s 81ms/step - loss: 0.4589 - accuracy: 0.8026 - val_loss: 0.4712 - val_accuracy: 0.8737
Epoch 32/150
3/3 [==============================] - 0s 71ms/step - loss: 0.3949 - accuracy: 0.8553 - val_loss: 0.4752 - val_accuracy: 0.8000
Epoch 132/150
3/3 [==============================] - 0s 62ms/step - loss: 0.4614 - accuracy: 0.7947 - val_loss: 0.4680 - val_accuracy: 0.8737
Epoch 33/150
3/3 [==============================] - 0s 55ms/step - loss: 0.4853 - accuracy: 0.7711 - val_loss: 0.4645 - val_accuracy: 0.8632
3/3 [==============================] - 0s 91ms/step - loss: 0.4162 - accuracy: 0.8316 - val_loss: 0.4753 - val_accuracy: 0.8000
Epoch 133/150
1/3 [=========>....................] - ETA: 0s - loss: 0.3466 - accuracy: 0.8828Epoch 34/150
3/3 [==============================] - 0s 64ms/step - loss: 0.4165 - accuracy: 0.8342 - val_loss: 0.4760 - val_accuracy: 0.7789
Epoch 134/150
3/3 [==============================] - 0s 111ms/step - loss: 0.4842 - accuracy: 0.7947 - val_loss: 0.4615 - val_accuracy: 0.8632
Epoch 35/150
3/3 [==============================] - 0s 59ms/step - loss: 0.4112 - accuracy: 0.8237 - val_loss: 0.4765 - val_accuracy: 0.7684
Epoch 135/150
3/3 [==============================] - 0s 66ms/step - loss: 0.4099 - accuracy: 0.8368 - val_loss: 0.4766 - val_accuracy: 0.7684
3/3 [==============================] - 0s 90ms/step - loss: 0.4793 - accuracy: 0.7895 - val_loss: 0.4591 - val_accuracy: 0.8632
Epoch 136/150
Epoch 36/150
3/3 [==============================] - 0s 77ms/step - loss: 0.4827 - accuracy: 0.7947 - val_loss: 0.4566 - val_accuracy: 0.8632
Epoch 37/150
3/3 [==============================] - 0s 109ms/step - loss: 0.3819 - accuracy: 0.8342 - val_loss: 0.4764 - val_accuracy: 0.7789
Epoch 137/150
3/3 [==============================] - 0s 68ms/step - loss: 0.4601 - accuracy: 0.7842 - val_loss: 0.4541 - val_accuracy: 0.8632
Epoch 38/150
3/3 [==============================] - 0s 89ms/step - loss: 0.4106 - accuracy: 0.8368 - val_loss: 0.4763 - val_accuracy: 0.7789
Epoch 138/150
3/3 [==============================] - 0s 53ms/step - loss: 0.4682 - accuracy: 0.7868 - val_loss: 0.4517 - val_accuracy: 0.8632
Epoch 39/150
3/3 [==============================] - 0s 89ms/step - loss: 0.4225 - accuracy: 0.8211 - val_loss: 0.4763 - val_accuracy: 0.7684
Epoch 139/150
3/3 [==============================] - 0s 90ms/step - loss: 0.4838 - accuracy: 0.7500 - val_loss: 0.4491 - val_accuracy: 0.8632
Epoch 40/150
3/3 [==============================] - 0s 52ms/step - loss: 0.4446 - accuracy: 0.7868 - val_loss: 0.4464 - val_accuracy: 0.8737
Epoch 41/150
3/3 [==============================] - 0s 83ms/step - loss: 0.4259 - accuracy: 0.8211 - val_loss: 0.4763 - val_accuracy: 0.7684
Epoch 140/150
3/3 [==============================] - 0s 67ms/step - loss: 0.4482 - accuracy: 0.7868 - val_loss: 0.4440 - val_accuracy: 0.8737
Epoch 42/150
3/3 [==============================] - 0s 66ms/step - loss: 0.3922 - accuracy: 0.8289 - val_loss: 0.4764 - val_accuracy: 0.7684
Epoch 141/150
3/3 [==============================] - 0s 75ms/step - loss: 0.4810 - accuracy: 0.7684 - val_loss: 0.4415 - val_accuracy: 0.8737
Epoch 43/150
3/3 [==============================] - 0s 69ms/step - loss: 0.4023 - accuracy: 0.8316 - val_loss: 0.4767 - val_accuracy: 0.7684
Epoch 142/150
3/3 [==============================] - 0s 53ms/step - loss: 0.4733 - accuracy: 0.7711 - val_loss: 0.4388 - val_accuracy: 0.8737
Epoch 44/150
3/3 [==============================] - 0s 83ms/step - loss: 0.3933 - accuracy: 0.8500 - val_loss: 0.4771 - val_accuracy: 0.7684
Epoch 143/150
3/3 [==============================] - 0s 69ms/step - loss: 0.4793 - accuracy: 0.7737 - val_loss: 0.4362 - val_accuracy: 0.8737
Epoch 45/150
3/3 [==============================] - 0s 70ms/step - loss: 0.3858 - accuracy: 0.8289 - val_loss: 0.4774 - val_accuracy: 0.7684
Epoch 144/150
3/3 [==============================] - 0s 68ms/step - loss: 0.4970 - accuracy: 0.8000 - val_loss: 0.4334 - val_accuracy: 0.8737
Epoch 46/150
3/3 [==============================] - 0s 72ms/step - loss: 0.3917 - accuracy: 0.8342 - val_loss: 0.4777 - val_accuracy: 0.7789
Epoch 145/150
3/3 [==============================] - 0s 80ms/step - loss: 0.4713 - accuracy: 0.7658 - val_loss: 0.4305 - val_accuracy: 0.8737
Epoch 47/150
3/3 [==============================] - 0s 85ms/step - loss: 0.3849 - accuracy: 0.8474 - val_loss: 0.4780 - val_accuracy: 0.7789
Epoch 146/150
3/3 [==============================] - 0s 75ms/step - loss: 0.4602 - accuracy: 0.7842 - val_loss: 0.4276 - val_accuracy: 0.8737
Epoch 48/150
3/3 [==============================] - 0s 80ms/step - loss: 0.4046 - accuracy: 0.8395 - val_loss: 0.4778 - val_accuracy: 0.7789
Epoch 147/150
3/3 [==============================] - 0s 88ms/step - loss: 0.4231 - accuracy: 0.7842 - val_loss: 0.4252 - val_accuracy: 0.8737
Epoch 49/150
3/3 [==============================] - 0s 67ms/step - loss: 0.4088 - accuracy: 0.8395 - val_loss: 0.4779 - val_accuracy: 0.7789
Epoch 148/150
3/3 [==============================] - 0s 71ms/step - loss: 0.4477 - accuracy: 0.7921 - val_loss: 0.4229 - val_accuracy: 0.8842
Epoch 50/150
3/3 [==============================] - 0s 74ms/step - loss: 0.4060 - accuracy: 0.8474 - val_loss: 0.4776 - val_accuracy: 0.7789
Epoch 149/150
3/3 [==============================] - 0s 71ms/step - loss: 0.4712 - accuracy: 0.7895 - val_loss: 0.4213 - val_accuracy: 0.8842
Epoch 51/150
3/3 [==============================] - 0s 86ms/step - loss: 0.4075 - accuracy: 0.8342 - val_loss: 0.4773 - val_accuracy: 0.7789
Epoch 150/150
3/3 [==============================] - 0s 84ms/step - loss: 0.4480 - accuracy: 0.7789 - val_loss: 0.4199 - val_accuracy: 0.8842
Epoch 52/150
3/3 [==============================] - 0s 83ms/step - loss: 0.3857 - accuracy: 0.8316 - val_loss: 0.4775 - val_accuracy: 0.7789
3/3 [==============================] - 0s 49ms/step - loss: 0.4298 - accuracy: 0.7921 - val_loss: 0.4183 - val_accuracy: 0.8842
Epoch 53/150
3/3 [==============================] - 0s 35ms/step - loss: 0.4459 - accuracy: 0.8105 - val_loss: 0.4168 - val_accuracy: 0.8842
Epoch 54/150
3/3 [==============================] - 0s 46ms/step - loss: 0.4383 - accuracy: 0.8026 - val_loss: 0.4154 - val_accuracy: 0.8737
Epoch 55/150
3/3 [==============================] - 0s 46ms/step - loss: 0.4879 - accuracy: 0.7789 - val_loss: 0.4137 - val_accuracy: 0.8737
Epoch 56/150
3/3 [==============================] - 0s 39ms/step - loss: 0.4697 - accuracy: 0.7816 - val_loss: 0.4121 - val_accuracy: 0.8737
Epoch 57/150
3/3 [==============================] - 0s 39ms/step - loss: 0.4567 - accuracy: 0.7895 - val_loss: 0.4105 - val_accuracy: 0.8737
Epoch 58/150
3/3 [==============================] - 0s 56ms/step - loss: 0.4571 - accuracy: 0.7974 - val_loss: 0.4091 - val_accuracy: 0.8737
Epoch 59/150
3/3 [==============================] - 0s 40ms/step - loss: 0.4452 - accuracy: 0.7895 - val_loss: 0.4077 - val_accuracy: 0.8737
Epoch 60/150
3/3 [==============================] - 0s 44ms/step - loss: 0.4687 - accuracy: 0.8053 - val_loss: 0.4062 - val_accuracy: 0.8737
Epoch 61/150
3/3 [==============================] - 0s 43ms/step - loss: 0.4641 - accuracy: 0.7763 - val_loss: 0.4047 - val_accuracy: 0.8737
Epoch 62/150
3/3 [==============================] - 0s 41ms/step - loss: 0.4295 - accuracy: 0.7974 - val_loss: 0.4032 - val_accuracy: 0.8737
Epoch 63/150
3/3 [==============================] - 0s 46ms/step - loss: 0.4780 - accuracy: 0.7737 - val_loss: 0.4017 - val_accuracy: 0.8737
Epoch 64/150
3/3 [==============================] - 0s 37ms/step - loss: 0.4490 - accuracy: 0.7974 - val_loss: 0.4004 - val_accuracy: 0.8737
Epoch 65/150
3/3 [==============================] - 0s 41ms/step - loss: 0.4317 - accuracy: 0.8105 - val_loss: 0.3989 - val_accuracy: 0.8737
Epoch 66/150
3/3 [==============================] - 0s 39ms/step - loss: 0.4304 - accuracy: 0.7947 - val_loss: 0.3974 - val_accuracy: 0.8737
Epoch 67/150
3/3 [==============================] - 0s 36ms/step - loss: 0.4828 - accuracy: 0.7658 - val_loss: 0.3963 - val_accuracy: 0.8737
Epoch 68/150
3/3 [==============================] - 0s 53ms/step - loss: 0.4331 - accuracy: 0.7684 - val_loss: 0.3950 - val_accuracy: 0.8737
Epoch 69/150
3/3 [==============================] - 0s 46ms/step - loss: 0.4608 - accuracy: 0.8000 - val_loss: 0.3936 - val_accuracy: 0.8737
Epoch 70/150
3/3 [==============================] - 0s 49ms/step - loss: 0.4596 - accuracy: 0.7816 - val_loss: 0.3921 - val_accuracy: 0.8737
Epoch 71/150
3/3 [==============================] - 0s 45ms/step - loss: 0.4426 - accuracy: 0.8053 - val_loss: 0.3903 - val_accuracy: 0.8737
Epoch 72/150
3/3 [==============================] - 0s 43ms/step - loss: 0.4539 - accuracy: 0.7842 - val_loss: 0.3890 - val_accuracy: 0.8737
Epoch 73/150
3/3 [==============================] - 0s 56ms/step - loss: 0.4408 - accuracy: 0.7947 - val_loss: 0.3878 - val_accuracy: 0.8737
Epoch 74/150
3/3 [==============================] - 0s 45ms/step - loss: 0.4490 - accuracy: 0.7947 - val_loss: 0.3868 - val_accuracy: 0.8737
Epoch 75/150
3/3 [==============================] - 0s 40ms/step - loss: 0.4213 - accuracy: 0.8184 - val_loss: 0.3859 - val_accuracy: 0.8737
Epoch 76/150
3/3 [==============================] - 0s 41ms/step - loss: 0.4492 - accuracy: 0.8026 - val_loss: 0.3850 - val_accuracy: 0.8737
Epoch 77/150
3/3 [==============================] - 0s 39ms/step - loss: 0.4377 - accuracy: 0.8237 - val_loss: 0.3840 - val_accuracy: 0.8737
Epoch 78/150
3/3 [==============================] - 0s 36ms/step - loss: 0.4469 - accuracy: 0.7763 - val_loss: 0.3830 - val_accuracy: 0.8737
Epoch 79/150
3/3 [==============================] - 0s 40ms/step - loss: 0.4743 - accuracy: 0.7737 - val_loss: 0.3821 - val_accuracy: 0.8737
Epoch 80/150
3/3 [==============================] - 0s 38ms/step - loss: 0.4338 - accuracy: 0.8000 - val_loss: 0.3810 - val_accuracy: 0.8737
Epoch 81/150
3/3 [==============================] - 0s 30ms/step - loss: 0.4397 - accuracy: 0.8026 - val_loss: 0.3798 - val_accuracy: 0.8842
Epoch 82/150
3/3 [==============================] - 0s 39ms/step - loss: 0.4311 - accuracy: 0.7947 - val_loss: 0.3783 - val_accuracy: 0.8842
Epoch 83/150
3/3 [==============================] - 0s 34ms/step - loss: 0.4683 - accuracy: 0.7895 - val_loss: 0.3771 - val_accuracy: 0.8842
Epoch 84/150
3/3 [==============================] - 0s 32ms/step - loss: 0.4580 - accuracy: 0.7947 - val_loss: 0.3762 - val_accuracy: 0.8842
Epoch 85/150
3/3 [==============================] - 0s 36ms/step - loss: 0.4170 - accuracy: 0.8053 - val_loss: 0.3755 - val_accuracy: 0.8842
Epoch 86/150
3/3 [==============================] - 0s 30ms/step - loss: 0.4232 - accuracy: 0.8079 - val_loss: 0.3745 - val_accuracy: 0.8842
Epoch 87/150
3/3 [==============================] - 0s 29ms/step - loss: 0.4368 - accuracy: 0.8000 - val_loss: 0.3740 - val_accuracy: 0.8842
Epoch 88/150
3/3 [==============================] - 0s 33ms/step - loss: 0.4421 - accuracy: 0.7921 - val_loss: 0.3735 - val_accuracy: 0.8842
Epoch 89/150
3/3 [==============================] - 0s 31ms/step - loss: 0.4644 - accuracy: 0.8079 - val_loss: 0.3730 - val_accuracy: 0.8842
Epoch 90/150
3/3 [==============================] - 0s 38ms/step - loss: 0.4335 - accuracy: 0.7947 - val_loss: 0.3724 - val_accuracy: 0.8842
Epoch 91/150
3/3 [==============================] - 0s 38ms/step - loss: 0.4295 - accuracy: 0.7947 - val_loss: 0.3720 - val_accuracy: 0.8842
Epoch 92/150
3/3 [==============================] - 0s 32ms/step - loss: 0.4256 - accuracy: 0.8026 - val_loss: 0.3721 - val_accuracy: 0.8842
Epoch 93/150
3/3 [==============================] - 0s 30ms/step - loss: 0.4214 - accuracy: 0.8132 - val_loss: 0.3716 - val_accuracy: 0.8842
Epoch 94/150
3/3 [==============================] - 0s 31ms/step - loss: 0.4492 - accuracy: 0.7921 - val_loss: 0.3711 - val_accuracy: 0.8737
Epoch 95/150
3/3 [==============================] - 0s 36ms/step - loss: 0.4273 - accuracy: 0.8211 - val_loss: 0.3707 - val_accuracy: 0.8737
Epoch 96/150
3/3 [==============================] - 0s 47ms/step - loss: 0.4386 - accuracy: 0.8211 - val_loss: 0.3703 - val_accuracy: 0.8737
Epoch 97/150
3/3 [==============================] - 0s 45ms/step - loss: 0.4151 - accuracy: 0.8000 - val_loss: 0.3701 - val_accuracy: 0.8737
Epoch 98/150
3/3 [==============================] - 0s 38ms/step - loss: 0.4172 - accuracy: 0.8158 - val_loss: 0.3699 - val_accuracy: 0.8737
Epoch 99/150
3/3 [==============================] - 0s 60ms/step - loss: 0.4417 - accuracy: 0.8053 - val_loss: 0.3695 - val_accuracy: 0.8842
Epoch 100/150
3/3 [==============================] - 0s 39ms/step - loss: 0.4278 - accuracy: 0.8237 - val_loss: 0.3694 - val_accuracy: 0.8842
Epoch 101/150
3/3 [==============================] - 0s 59ms/step - loss: 0.4721 - accuracy: 0.7868 - val_loss: 0.3691 - val_accuracy: 0.8842
Epoch 102/150
3/3 [==============================] - 0s 35ms/step - loss: 0.4348 - accuracy: 0.8158 - val_loss: 0.3688 - val_accuracy: 0.8842
Epoch 103/150
3/3 [==============================] - 0s 38ms/step - loss: 0.4271 - accuracy: 0.7947 - val_loss: 0.3685 - val_accuracy: 0.8842
Epoch 104/150
3/3 [==============================] - 0s 32ms/step - loss: 0.4536 - accuracy: 0.7868 - val_loss: 0.3682 - val_accuracy: 0.8842
Epoch 105/150
3/3 [==============================] - 0s 41ms/step - loss: 0.4167 - accuracy: 0.8237 - val_loss: 0.3677 - val_accuracy: 0.8842
Epoch 106/150
3/3 [==============================] - 0s 38ms/step - loss: 0.4477 - accuracy: 0.7921 - val_loss: 0.3677 - val_accuracy: 0.8842
Epoch 107/150
3/3 [==============================] - 0s 43ms/step - loss: 0.4308 - accuracy: 0.7816 - val_loss: 0.3674 - val_accuracy: 0.8842
Epoch 108/150
3/3 [==============================] - 0s 48ms/step - loss: 0.4243 - accuracy: 0.7947 - val_loss: 0.3673 - val_accuracy: 0.8842
Epoch 109/150
3/3 [==============================] - 0s 29ms/step - loss: 0.4238 - accuracy: 0.8000 - val_loss: 0.3673 - val_accuracy: 0.8842
Epoch 110/150
3/3 [==============================] - 0s 28ms/step - loss: 0.4493 - accuracy: 0.7816 - val_loss: 0.3674 - val_accuracy: 0.8842
Epoch 111/150
3/3 [==============================] - 0s 38ms/step - loss: 0.4653 - accuracy: 0.8000 - val_loss: 0.3677 - val_accuracy: 0.8842
Epoch 112/150
3/3 [==============================] - 0s 40ms/step - loss: 0.4777 - accuracy: 0.7921 - val_loss: 0.3677 - val_accuracy: 0.8842
Epoch 113/150
3/3 [==============================] - 0s 29ms/step - loss: 0.4399 - accuracy: 0.7789 - val_loss: 0.3677 - val_accuracy: 0.8842
Epoch 114/150
3/3 [==============================] - 0s 39ms/step - loss: 0.4045 - accuracy: 0.8342 - val_loss: 0.3672 - val_accuracy: 0.8842
Epoch 115/150
3/3 [==============================] - 0s 40ms/step - loss: 0.4165 - accuracy: 0.8053 - val_loss: 0.3661 - val_accuracy: 0.8842
Epoch 116/150
3/3 [==============================] - 0s 31ms/step - loss: 0.4133 - accuracy: 0.8079 - val_loss: 0.3650 - val_accuracy: 0.8842
Epoch 117/150
3/3 [==============================] - 0s 39ms/step - loss: 0.3804 - accuracy: 0.8316 - val_loss: 0.3643 - val_accuracy: 0.8842
Epoch 118/150
3/3 [==============================] - 0s 39ms/step - loss: 0.4278 - accuracy: 0.7816 - val_loss: 0.3638 - val_accuracy: 0.8842
Epoch 119/150
3/3 [==============================] - 0s 38ms/step - loss: 0.4276 - accuracy: 0.7921 - val_loss: 0.3638 - val_accuracy: 0.8842
Epoch 120/150
3/3 [==============================] - 0s 41ms/step - loss: 0.3999 - accuracy: 0.8158 - val_loss: 0.3641 - val_accuracy: 0.8842
Epoch 121/150
3/3 [==============================] - 0s 40ms/step - loss: 0.4026 - accuracy: 0.8026 - val_loss: 0.3645 - val_accuracy: 0.8842
Epoch 122/150
3/3 [==============================] - 0s 31ms/step - loss: 0.4267 - accuracy: 0.8158 - val_loss: 0.3648 - val_accuracy: 0.8842
Epoch 123/150
3/3 [==============================] - 0s 38ms/step - loss: 0.4400 - accuracy: 0.8079 - val_loss: 0.3650 - val_accuracy: 0.8842
Epoch 124/150
3/3 [==============================] - 0s 37ms/step - loss: 0.4237 - accuracy: 0.7974 - val_loss: 0.3652 - val_accuracy: 0.8842
Epoch 125/150
3/3 [==============================] - 0s 38ms/step - loss: 0.3880 - accuracy: 0.8211 - val_loss: 0.3653 - val_accuracy: 0.8842
Epoch 126/150
3/3 [==============================] - 0s 39ms/step - loss: 0.3758 - accuracy: 0.8342 - val_loss: 0.3654 - val_accuracy: 0.8842
Epoch 127/150
3/3 [==============================] - 0s 30ms/step - loss: 0.4200 - accuracy: 0.8079 - val_loss: 0.3654 - val_accuracy: 0.8842
Epoch 128/150
3/3 [==============================] - 0s 38ms/step - loss: 0.4189 - accuracy: 0.8053 - val_loss: 0.3651 - val_accuracy: 0.8842
Epoch 129/150
3/3 [==============================] - 0s 41ms/step - loss: 0.4465 - accuracy: 0.7816 - val_loss: 0.3652 - val_accuracy: 0.8842
Epoch 130/150
3/3 [==============================] - 0s 46ms/step - loss: 0.4289 - accuracy: 0.8211 - val_loss: 0.3648 - val_accuracy: 0.8842
Epoch 131/150
3/3 [==============================] - 0s 47ms/step - loss: 0.4274 - accuracy: 0.7921 - val_loss: 0.3640 - val_accuracy: 0.8842
Epoch 132/150
3/3 [==============================] - 0s 44ms/step - loss: 0.4227 - accuracy: 0.8079 - val_loss: 0.3633 - val_accuracy: 0.8842
Epoch 133/150
3/3 [==============================] - 0s 47ms/step - loss: 0.4403 - accuracy: 0.7895 - val_loss: 0.3628 - val_accuracy: 0.8842
Epoch 134/150
3/3 [==============================] - 0s 47ms/step - loss: 0.4309 - accuracy: 0.8000 - val_loss: 0.3629 - val_accuracy: 0.8842
Epoch 135/150
3/3 [==============================] - 0s 47ms/step - loss: 0.4251 - accuracy: 0.8158 - val_loss: 0.3630 - val_accuracy: 0.8842
Epoch 136/150
3/3 [==============================] - 0s 41ms/step - loss: 0.4056 - accuracy: 0.8158 - val_loss: 0.3627 - val_accuracy: 0.8842
Epoch 137/150
3/3 [==============================] - 0s 29ms/step - loss: 0.4372 - accuracy: 0.7974 - val_loss: 0.3623 - val_accuracy: 0.8842
Epoch 138/150
3/3 [==============================] - 0s 38ms/step - loss: 0.4331 - accuracy: 0.8132 - val_loss: 0.3619 - val_accuracy: 0.8842
Epoch 139/150
3/3 [==============================] - 0s 37ms/step - loss: 0.4161 - accuracy: 0.8132 - val_loss: 0.3613 - val_accuracy: 0.8842
Epoch 140/150
3/3 [==============================] - 0s 30ms/step - loss: 0.4197 - accuracy: 0.8000 - val_loss: 0.3609 - val_accuracy: 0.8842
Epoch 141/150
3/3 [==============================] - 0s 37ms/step - loss: 0.4293 - accuracy: 0.8289 - val_loss: 0.3601 - val_accuracy: 0.8842
Epoch 142/150
3/3 [==============================] - 0s 28ms/step - loss: 0.4204 - accuracy: 0.8026 - val_loss: 0.3596 - val_accuracy: 0.8842
Epoch 143/150
3/3 [==============================] - 0s 44ms/step - loss: 0.4062 - accuracy: 0.8184 - val_loss: 0.3596 - val_accuracy: 0.8842
Epoch 144/150
3/3 [==============================] - 0s 32ms/step - loss: 0.4117 - accuracy: 0.8000 - val_loss: 0.3593 - val_accuracy: 0.8842
Epoch 145/150
3/3 [==============================] - 0s 35ms/step - loss: 0.4400 - accuracy: 0.7895 - val_loss: 0.3591 - val_accuracy: 0.8842
Epoch 146/150
3/3 [==============================] - 0s 37ms/step - loss: 0.4062 - accuracy: 0.8211 - val_loss: 0.3591 - val_accuracy: 0.8842
Epoch 147/150
3/3 [==============================] - 0s 37ms/step - loss: 0.4305 - accuracy: 0.8263 - val_loss: 0.3594 - val_accuracy: 0.8842
Epoch 148/150
3/3 [==============================] - 0s 39ms/step - loss: 0.4262 - accuracy: 0.7895 - val_loss: 0.3594 - val_accuracy: 0.8842
Epoch 149/150
3/3 [==============================] - 0s 29ms/step - loss: 0.4332 - accuracy: 0.7868 - val_loss: 0.3594 - val_accuracy: 0.8842
Epoch 150/150
3/3 [==============================] - 0s 38ms/step - loss: 0.4242 - accuracy: 0.8079 - val_loss: 0.3593 - val_accuracy: 0.8842
2/2 [==============================] - 0s 13ms/step - loss: 0.4293 - accuracy: 0.8397
Epoch 1/150
3/3 [==============================] - 2s 200ms/step - loss: 0.7605 - accuracy: 0.6121 - val_loss: 0.6653 - val_accuracy: 0.6421
Epoch 2/150
3/3 [==============================] - 0s 42ms/step - loss: 0.6350 - accuracy: 0.6755 - val_loss: 0.6416 - val_accuracy: 0.6526
Epoch 3/150
3/3 [==============================] - 0s 27ms/step - loss: 0.6291 - accuracy: 0.6834 - val_loss: 0.6229 - val_accuracy: 0.6737
Epoch 4/150
3/3 [==============================] - 0s 38ms/step - loss: 0.5536 - accuracy: 0.7177 - val_loss: 0.6089 - val_accuracy: 0.6947
Epoch 5/150
3/3 [==============================] - 0s 36ms/step - loss: 0.5755 - accuracy: 0.7361 - val_loss: 0.5980 - val_accuracy: 0.7158
Epoch 6/150
3/3 [==============================] - 0s 37ms/step - loss: 0.5023 - accuracy: 0.7625 - val_loss: 0.5884 - val_accuracy: 0.7158
Epoch 7/150
3/3 [==============================] - 0s 27ms/step - loss: 0.5059 - accuracy: 0.7652 - val_loss: 0.5807 - val_accuracy: 0.7263
Epoch 8/150
3/3 [==============================] - 0s 40ms/step - loss: 0.5025 - accuracy: 0.7889 - val_loss: 0.5741 - val_accuracy: 0.7368
Epoch 9/150
3/3 [==============================] - 0s 47ms/step - loss: 0.5165 - accuracy: 0.7810 - val_loss: 0.5691 - val_accuracy: 0.7158
Epoch 10/150
3/3 [==============================] - 0s 50ms/step - loss: 0.5150 - accuracy: 0.7704 - val_loss: 0.5651 - val_accuracy: 0.7263
Epoch 11/150
3/3 [==============================] - 0s 44ms/step - loss: 0.4820 - accuracy: 0.7731 - val_loss: 0.5616 - val_accuracy: 0.7263
Epoch 12/150
3/3 [==============================] - 0s 58ms/step - loss: 0.4544 - accuracy: 0.8074 - val_loss: 0.5588 - val_accuracy: 0.7263
Epoch 13/150
3/3 [==============================] - 0s 47ms/step - loss: 0.4797 - accuracy: 0.7625 - val_loss: 0.5562 - val_accuracy: 0.7368
Epoch 14/150
3/3 [==============================] - 0s 51ms/step - loss: 0.4247 - accuracy: 0.8153 - val_loss: 0.5540 - val_accuracy: 0.7368
Epoch 15/150
3/3 [==============================] - 0s 40ms/step - loss: 0.4498 - accuracy: 0.7810 - val_loss: 0.5514 - val_accuracy: 0.7368
Epoch 16/150
3/3 [==============================] - 0s 43ms/step - loss: 0.4447 - accuracy: 0.8179 - val_loss: 0.5490 - val_accuracy: 0.7368
Epoch 17/150
3/3 [==============================] - 0s 45ms/step - loss: 0.4296 - accuracy: 0.8153 - val_loss: 0.5468 - val_accuracy: 0.7474
Epoch 18/150
3/3 [==============================] - 0s 47ms/step - loss: 0.4344 - accuracy: 0.8074 - val_loss: 0.5447 - val_accuracy: 0.7474
Epoch 19/150
3/3 [==============================] - 0s 63ms/step - loss: 0.4116 - accuracy: 0.8311 - val_loss: 0.5428 - val_accuracy: 0.7579
Epoch 20/150
3/3 [==============================] - 0s 55ms/step - loss: 0.4520 - accuracy: 0.7968 - val_loss: 0.5413 - val_accuracy: 0.7579
Epoch 21/150
3/3 [==============================] - 0s 46ms/step - loss: 0.4184 - accuracy: 0.8206 - val_loss: 0.5397 - val_accuracy: 0.7579
Epoch 22/150
3/3 [==============================] - 0s 51ms/step - loss: 0.4245 - accuracy: 0.8179 - val_loss: 0.5379 - val_accuracy: 0.7579
Epoch 23/150
3/3 [==============================] - 0s 61ms/step - loss: 0.4352 - accuracy: 0.8259 - val_loss: 0.5364 - val_accuracy: 0.7579
Epoch 24/150
3/3 [==============================] - 0s 55ms/step - loss: 0.4253 - accuracy: 0.8127 - val_loss: 0.5347 - val_accuracy: 0.7579
Epoch 25/150
3/3 [==============================] - 0s 58ms/step - loss: 0.4135 - accuracy: 0.8259 - val_loss: 0.5333 - val_accuracy: 0.7579
Epoch 26/150
3/3 [==============================] - 0s 53ms/step - loss: 0.4322 - accuracy: 0.8127 - val_loss: 0.5318 - val_accuracy: 0.7579
Epoch 27/150
3/3 [==============================] - 0s 49ms/step - loss: 0.4151 - accuracy: 0.8338 - val_loss: 0.5298 - val_accuracy: 0.7579
Epoch 28/150
3/3 [==============================] - 0s 45ms/step - loss: 0.3886 - accuracy: 0.8417 - val_loss: 0.5282 - val_accuracy: 0.7474
Epoch 29/150
3/3 [==============================] - 0s 52ms/step - loss: 0.4248 - accuracy: 0.8179 - val_loss: 0.5267 - val_accuracy: 0.7474
Epoch 30/150
3/3 [==============================] - 0s 51ms/step - loss: 0.4001 - accuracy: 0.8285 - val_loss: 0.5252 - val_accuracy: 0.7474
Epoch 31/150
3/3 [==============================] - 0s 50ms/step - loss: 0.3832 - accuracy: 0.8575 - val_loss: 0.5241 - val_accuracy: 0.7474
Epoch 32/150
3/3 [==============================] - 0s 49ms/step - loss: 0.4018 - accuracy: 0.8153 - val_loss: 0.5229 - val_accuracy: 0.7474
Epoch 33/150
3/3 [==============================] - 0s 49ms/step - loss: 0.4211 - accuracy: 0.7995 - val_loss: 0.5217 - val_accuracy: 0.7474
Epoch 34/150
3/3 [==============================] - 0s 61ms/step - loss: 0.3777 - accuracy: 0.8364 - val_loss: 0.5205 - val_accuracy: 0.7579
Epoch 35/150
3/3 [==============================] - 0s 49ms/step - loss: 0.3705 - accuracy: 0.8364 - val_loss: 0.5193 - val_accuracy: 0.7579
Epoch 36/150
3/3 [==============================] - 0s 52ms/step - loss: 0.3893 - accuracy: 0.8285 - val_loss: 0.5185 - val_accuracy: 0.7579
Epoch 37/150
3/3 [==============================] - 0s 45ms/step - loss: 0.3623 - accuracy: 0.8602 - val_loss: 0.5173 - val_accuracy: 0.7579
Epoch 38/150
3/3 [==============================] - 0s 49ms/step - loss: 0.3863 - accuracy: 0.8391 - val_loss: 0.5163 - val_accuracy: 0.7579
Epoch 39/150
3/3 [==============================] - 0s 47ms/step - loss: 0.3979 - accuracy: 0.8285 - val_loss: 0.5151 - val_accuracy: 0.7579
Epoch 40/150
3/3 [==============================] - 0s 50ms/step - loss: 0.3983 - accuracy: 0.8338 - val_loss: 0.5145 - val_accuracy: 0.7579
Epoch 41/150
3/3 [==============================] - 0s 56ms/step - loss: 0.3950 - accuracy: 0.8259 - val_loss: 0.5144 - val_accuracy: 0.7579
Epoch 42/150
3/3 [==============================] - 0s 37ms/step - loss: 0.3659 - accuracy: 0.8391 - val_loss: 0.5138 - val_accuracy: 0.7579
Epoch 43/150
3/3 [==============================] - 0s 49ms/step - loss: 0.3902 - accuracy: 0.8338 - val_loss: 0.5132 - val_accuracy: 0.7579
Epoch 44/150
3/3 [==============================] - 0s 47ms/step - loss: 0.3945 - accuracy: 0.8364 - val_loss: 0.5126 - val_accuracy: 0.7684
Epoch 45/150
3/3 [==============================] - 0s 48ms/step - loss: 0.3966 - accuracy: 0.8311 - val_loss: 0.5121 - val_accuracy: 0.7684
Epoch 46/150
3/3 [==============================] - 0s 52ms/step - loss: 0.4255 - accuracy: 0.8206 - val_loss: 0.5117 - val_accuracy: 0.7684
Epoch 47/150
3/3 [==============================] - 0s 51ms/step - loss: 0.3505 - accuracy: 0.8391 - val_loss: 0.5115 - val_accuracy: 0.7684
Epoch 48/150
3/3 [==============================] - 0s 46ms/step - loss: 0.3473 - accuracy: 0.8496 - val_loss: 0.5114 - val_accuracy: 0.7684
Epoch 49/150
3/3 [==============================] - 0s 56ms/step - loss: 0.3871 - accuracy: 0.8391 - val_loss: 0.5114 - val_accuracy: 0.7684
Epoch 50/150
3/3 [==============================] - 0s 55ms/step - loss: 0.3655 - accuracy: 0.8470 - val_loss: 0.5103 - val_accuracy: 0.7684
Epoch 51/150
3/3 [==============================] - 0s 52ms/step - loss: 0.3630 - accuracy: 0.8470 - val_loss: 0.5094 - val_accuracy: 0.7684
Epoch 52/150
3/3 [==============================] - 0s 56ms/step - loss: 0.3680 - accuracy: 0.8443 - val_loss: 0.5082 - val_accuracy: 0.7789
Epoch 53/150
3/3 [==============================] - 0s 61ms/step - loss: 0.3823 - accuracy: 0.8496 - val_loss: 0.5072 - val_accuracy: 0.7895
Epoch 54/150
3/3 [==============================] - 0s 38ms/step - loss: 0.3655 - accuracy: 0.8364 - val_loss: 0.5064 - val_accuracy: 0.7789
Epoch 55/150
3/3 [==============================] - 0s 47ms/step - loss: 0.3741 - accuracy: 0.8496 - val_loss: 0.5058 - val_accuracy: 0.7789
Epoch 56/150
3/3 [==============================] - 0s 51ms/step - loss: 0.3758 - accuracy: 0.8338 - val_loss: 0.5046 - val_accuracy: 0.7789
Epoch 57/150
3/3 [==============================] - 0s 45ms/step - loss: 0.3694 - accuracy: 0.8417 - val_loss: 0.5037 - val_accuracy: 0.7789
Epoch 58/150
3/3 [==============================] - 0s 79ms/step - loss: 0.3634 - accuracy: 0.8443 - val_loss: 0.5029 - val_accuracy: 0.7789
Epoch 59/150
3/3 [==============================] - 0s 79ms/step - loss: 0.3741 - accuracy: 0.8470 - val_loss: 0.5023 - val_accuracy: 0.7789
Epoch 60/150
2/2 [==============================] - 0s 11ms/step - loss: 0.3939 - accuracy: 0.8397
3/3 [==============================] - 0s 94ms/step - loss: 0.3282 - accuracy: 0.8575 - val_loss: 0.5020 - val_accuracy: 0.7789
Epoch 61/150
3/3 [==============================] - 0s 105ms/step - loss: 0.3789 - accuracy: 0.8470 - val_loss: 0.5020 - val_accuracy: 0.7789
Epoch 62/150
3/3 [==============================] - 0s 85ms/step - loss: 0.3425 - accuracy: 0.8575 - val_loss: 0.5019 - val_accuracy: 0.7789
Epoch 63/150
3/3 [==============================] - 0s 77ms/step - loss: 0.3758 - accuracy: 0.8522 - val_loss: 0.5017 - val_accuracy: 0.7789
Epoch 64/150
1/3 [=========>....................] - ETA: 0s - loss: 0.3282 - accuracy: 0.8750Epoch 1/150
3/3 [==============================] - 0s 74ms/step - loss: 0.3403 - accuracy: 0.8602 - val_loss: 0.5019 - val_accuracy: 0.7789
Epoch 65/150
3/3 [==============================] - 0s 65ms/step - loss: 0.3603 - accuracy: 0.8522 - val_loss: 0.5027 - val_accuracy: 0.7789
Epoch 66/150
3/3 [==============================] - 0s 62ms/step - loss: 0.3646 - accuracy: 0.8417 - val_loss: 0.5032 - val_accuracy: 0.7789
Epoch 67/150
3/3 [==============================] - 3s 2s/step - loss: 0.3776 - accuracy: 0.8575 - val_loss: 0.5033 - val_accuracy: 0.7789
Epoch 68/150
3/3 [==============================] - 0s 98ms/step - loss: 0.3718 - accuracy: 0.8602 - val_loss: 0.5038 - val_accuracy: 0.7789
Epoch 69/150
3/3 [==============================] - 0s 88ms/step - loss: 0.3637 - accuracy: 0.8575 - val_loss: 0.5041 - val_accuracy: 0.7789
Epoch 70/150
3/3 [==============================] - 0s 85ms/step - loss: 0.3417 - accuracy: 0.8681 - val_loss: 0.5047 - val_accuracy: 0.7789
Epoch 71/150
3/3 [==============================] - 0s 94ms/step - loss: 0.3618 - accuracy: 0.8602 - val_loss: 0.5051 - val_accuracy: 0.7789
Epoch 72/150
3/3 [==============================] - 0s 94ms/step - loss: 0.3456 - accuracy: 0.8602 - val_loss: 0.5054 - val_accuracy: 0.7789
Epoch 73/150
3/3 [==============================] - 0s 94ms/step - loss: 0.3566 - accuracy: 0.8628 - val_loss: 0.5060 - val_accuracy: 0.7789
Epoch 74/150
3/3 [==============================] - 0s 106ms/step - loss: 0.3672 - accuracy: 0.8522 - val_loss: 0.5061 - val_accuracy: 0.7789
Epoch 75/150
3/3 [==============================] - 0s 88ms/step - loss: 0.3457 - accuracy: 0.8575 - val_loss: 0.5061 - val_accuracy: 0.7789
Epoch 76/150
3/3 [==============================] - 0s 97ms/step - loss: 0.3513 - accuracy: 0.8417 - val_loss: 0.5060 - val_accuracy: 0.7789
Epoch 77/150
3/3 [==============================] - 0s 90ms/step - loss: 0.3636 - accuracy: 0.8496 - val_loss: 0.5058 - val_accuracy: 0.7789
Epoch 78/150
3/3 [==============================] - 0s 101ms/step - loss: 0.3475 - accuracy: 0.8602 - val_loss: 0.5060 - val_accuracy: 0.7895
Epoch 79/150
3/3 [==============================] - 0s 88ms/step - loss: 0.3664 - accuracy: 0.8575 - val_loss: 0.5057 - val_accuracy: 0.7895
Epoch 80/150
3/3 [==============================] - 0s 95ms/step - loss: 0.3519 - accuracy: 0.8602 - val_loss: 0.5064 - val_accuracy: 0.7895
Epoch 81/150
3/3 [==============================] - 0s 70ms/step - loss: 0.3491 - accuracy: 0.8654 - val_loss: 0.5069 - val_accuracy: 0.7895
Epoch 82/150
3/3 [==============================] - 0s 93ms/step - loss: 0.3367 - accuracy: 0.8628 - val_loss: 0.5073 - val_accuracy: 0.7895
Epoch 83/150
3/3 [==============================] - 0s 94ms/step - loss: 0.3538 - accuracy: 0.8575 - val_loss: 0.5081 - val_accuracy: 0.7895
Epoch 84/150
3/3 [==============================] - 0s 99ms/step - loss: 0.3722 - accuracy: 0.8338 - val_loss: 0.5088 - val_accuracy: 0.8000
Epoch 85/150
3/3 [==============================] - 0s 97ms/step - loss: 0.3438 - accuracy: 0.8654 - val_loss: 0.5094 - val_accuracy: 0.8000
Epoch 86/150
3/3 [==============================] - 0s 89ms/step - loss: 0.3359 - accuracy: 0.8549 - val_loss: 0.5100 - val_accuracy: 0.8000
Epoch 87/150
3/3 [==============================] - 0s 90ms/step - loss: 0.3494 - accuracy: 0.8470 - val_loss: 0.5105 - val_accuracy: 0.7895
Epoch 88/150
3/3 [==============================] - 0s 97ms/step - loss: 0.3369 - accuracy: 0.8654 - val_loss: 0.5108 - val_accuracy: 0.7895
Epoch 89/150
3/3 [==============================] - 0s 108ms/step - loss: 0.3321 - accuracy: 0.8575 - val_loss: 0.5116 - val_accuracy: 0.7895
Epoch 90/150
3/3 [==============================] - 0s 107ms/step - loss: 0.3768 - accuracy: 0.8549 - val_loss: 0.5125 - val_accuracy: 0.7895
Epoch 91/150
3/3 [==============================] - 0s 113ms/step - loss: 0.3867 - accuracy: 0.8417 - val_loss: 0.5129 - val_accuracy: 0.8105
Epoch 92/150
3/3 [==============================] - 0s 66ms/step - loss: 0.3309 - accuracy: 0.8707 - val_loss: 0.5131 - val_accuracy: 0.8105
Epoch 93/150
3/3 [==============================] - 0s 83ms/step - loss: 0.3516 - accuracy: 0.8443 - val_loss: 0.5137 - val_accuracy: 0.7895
Epoch 94/150
3/3 [==============================] - 0s 94ms/step - loss: 0.3371 - accuracy: 0.8734 - val_loss: 0.5143 - val_accuracy: 0.7895
Epoch 95/150
3/3 [==============================] - 9s 633ms/step - loss: 0.8575 - accuracy: 0.5211 - val_loss: 0.6468 - val_accuracy: 0.6842
Epoch 2/150
3/3 [==============================] - 0s 74ms/step - loss: 0.3486 - accuracy: 0.8575 - val_loss: 0.5146 - val_accuracy: 0.7895
Epoch 96/150
3/3 [==============================] - 0s 75ms/step - loss: 0.3465 - accuracy: 0.8522 - val_loss: 0.5152 - val_accuracy: 0.8105
3/3 [==============================] - 0s 83ms/step - loss: 0.6938 - accuracy: 0.6263 - val_loss: 0.6282 - val_accuracy: 0.7158
Epoch 97/150
Epoch 3/150
3/3 [==============================] - 0s 67ms/step - loss: 0.3820 - accuracy: 0.8338 - val_loss: 0.5162 - val_accuracy: 0.8105
Epoch 98/150
3/3 [==============================] - 0s 71ms/step - loss: 0.6807 - accuracy: 0.6421 - val_loss: 0.6143 - val_accuracy: 0.7263
Epoch 4/150
3/3 [==============================] - 0s 81ms/step - loss: 0.3694 - accuracy: 0.8522 - val_loss: 0.5169 - val_accuracy: 0.8105
Epoch 99/150
3/3 [==============================] - 0s 74ms/step - loss: 0.6147 - accuracy: 0.7000 - val_loss: 0.6028 - val_accuracy: 0.7474
Epoch 5/150
3/3 [==============================] - 0s 82ms/step - loss: 0.3688 - accuracy: 0.8522 - val_loss: 0.5178 - val_accuracy: 0.8105
Epoch 100/150
3/3 [==============================] - 0s 80ms/step - loss: 0.5926 - accuracy: 0.6842 - val_loss: 0.5939 - val_accuracy: 0.7263
Epoch 6/150
3/3 [==============================] - 0s 73ms/step - loss: 0.6120 - accuracy: 0.7158 - val_loss: 0.5867 - val_accuracy: 0.7158
3/3 [==============================] - 0s 94ms/step - loss: 0.3271 - accuracy: 0.8496 - val_loss: 0.5185 - val_accuracy: 0.8105
Epoch 101/150
Epoch 7/150
3/3 [==============================] - 0s 90ms/step - loss: 0.6487 - accuracy: 0.6763 - val_loss: 0.5802 - val_accuracy: 0.7263
Epoch 8/150
3/3 [==============================] - 0s 118ms/step - loss: 0.3548 - accuracy: 0.8443 - val_loss: 0.5185 - val_accuracy: 0.8105
Epoch 102/150
3/3 [==============================] - 0s 111ms/step - loss: 0.5453 - accuracy: 0.7263 - val_loss: 0.5743 - val_accuracy: 0.7368
Epoch 9/150
3/3 [==============================] - 0s 133ms/step - loss: 0.3227 - accuracy: 0.8681 - val_loss: 0.5181 - val_accuracy: 0.8105
Epoch 103/150
3/3 [==============================] - 0s 98ms/step - loss: 0.5435 - accuracy: 0.7421 - val_loss: 0.5689 - val_accuracy: 0.7263
Epoch 10/150
3/3 [==============================] - 0s 100ms/step - loss: 0.3278 - accuracy: 0.8496 - val_loss: 0.5180 - val_accuracy: 0.8105
Epoch 104/150
3/3 [==============================] - 0s 89ms/step - loss: 0.5307 - accuracy: 0.7763 - val_loss: 0.5638 - val_accuracy: 0.7368
Epoch 11/150
3/3 [==============================] - 0s 93ms/step - loss: 0.3314 - accuracy: 0.8470 - val_loss: 0.5183 - val_accuracy: 0.8105
Epoch 105/150
3/3 [==============================] - 0s 80ms/step - loss: 0.5372 - accuracy: 0.7763 - val_loss: 0.5588 - val_accuracy: 0.7579
Epoch 12/150
3/3 [==============================] - 0s 93ms/step - loss: 0.3568 - accuracy: 0.8786 - val_loss: 0.5181 - val_accuracy: 0.8105
Epoch 106/150
3/3 [==============================] - 0s 86ms/step - loss: 0.5135 - accuracy: 0.7447 - val_loss: 0.5547 - val_accuracy: 0.7684
Epoch 13/150
3/3 [==============================] - 0s 98ms/step - loss: 0.3400 - accuracy: 0.8707 - val_loss: 0.5179 - val_accuracy: 0.8000
Epoch 107/150
3/3 [==============================] - 0s 80ms/step - loss: 0.5171 - accuracy: 0.7658 - val_loss: 0.5504 - val_accuracy: 0.7684
1/3 [=========>....................] - ETA: 0s - loss: 0.3313 - accuracy: 0.8672Epoch 14/150
3/3 [==============================] - 0s 67ms/step - loss: 0.4955 - accuracy: 0.7868 - val_loss: 0.5463 - val_accuracy: 0.7789
Epoch 15/150
3/3 [==============================] - 0s 123ms/step - loss: 0.3568 - accuracy: 0.8602 - val_loss: 0.5179 - val_accuracy: 0.8000
Epoch 108/150
3/3 [==============================] - 0s 79ms/step - loss: 0.5012 - accuracy: 0.7579 - val_loss: 0.5424 - val_accuracy: 0.7789
Epoch 16/150
3/3 [==============================] - 0s 83ms/step - loss: 0.3160 - accuracy: 0.8865 - val_loss: 0.5180 - val_accuracy: 0.8000
Epoch 109/150
3/3 [==============================] - 0s 85ms/step - loss: 0.5255 - accuracy: 0.7579 - val_loss: 0.5385 - val_accuracy: 0.7789
Epoch 17/150
3/3 [==============================] - 0s 90ms/step - loss: 0.3498 - accuracy: 0.8522 - val_loss: 0.5183 - val_accuracy: 0.8000
Epoch 110/150
3/3 [==============================] - 0s 93ms/step - loss: 0.4898 - accuracy: 0.7842 - val_loss: 0.5346 - val_accuracy: 0.7684
Epoch 18/150
3/3 [==============================] - 0s 99ms/step - loss: 0.3573 - accuracy: 0.8681 - val_loss: 0.5195 - val_accuracy: 0.8000
Epoch 111/150
3/3 [==============================] - 0s 105ms/step - loss: 0.5002 - accuracy: 0.7658 - val_loss: 0.5317 - val_accuracy: 0.7895
Epoch 19/150
3/3 [==============================] - 0s 108ms/step - loss: 0.3559 - accuracy: 0.8707 - val_loss: 0.5207 - val_accuracy: 0.8000
Epoch 112/150
3/3 [==============================] - 0s 123ms/step - loss: 0.5133 - accuracy: 0.7789 - val_loss: 0.5289 - val_accuracy: 0.8000
Epoch 20/150
3/3 [==============================] - 0s 120ms/step - loss: 0.3407 - accuracy: 0.8602 - val_loss: 0.5213 - val_accuracy: 0.8000
Epoch 113/150
3/3 [==============================] - 0s 105ms/step - loss: 0.4857 - accuracy: 0.7842 - val_loss: 0.5267 - val_accuracy: 0.8000
Epoch 21/150
3/3 [==============================] - 0s 120ms/step - loss: 0.3490 - accuracy: 0.8654 - val_loss: 0.5222 - val_accuracy: 0.8000
Epoch 114/150
3/3 [==============================] - 0s 101ms/step - loss: 0.5071 - accuracy: 0.7921 - val_loss: 0.5241 - val_accuracy: 0.8000
Epoch 22/150
3/3 [==============================] - 0s 94ms/step - loss: 0.3474 - accuracy: 0.8522 - val_loss: 0.5234 - val_accuracy: 0.8000
Epoch 115/150
3/3 [==============================] - 0s 84ms/step - loss: 0.4841 - accuracy: 0.7868 - val_loss: 0.5221 - val_accuracy: 0.8000
Epoch 23/150
3/3 [==============================] - 0s 99ms/step - loss: 0.3375 - accuracy: 0.8628 - val_loss: 0.5239 - val_accuracy: 0.8000
Epoch 116/150
3/3 [==============================] - 0s 81ms/step - loss: 0.5022 - accuracy: 0.7947 - val_loss: 0.5201 - val_accuracy: 0.8000
Epoch 24/150
3/3 [==============================] - 0s 101ms/step - loss: 0.3113 - accuracy: 0.8628 - val_loss: 0.5239 - val_accuracy: 0.8000
Epoch 117/150
3/3 [==============================] - 0s 88ms/step - loss: 0.4717 - accuracy: 0.7921 - val_loss: 0.5177 - val_accuracy: 0.8000
Epoch 25/150
3/3 [==============================] - 0s 64ms/step - loss: 0.4415 - accuracy: 0.8184 - val_loss: 0.5154 - val_accuracy: 0.8000
Epoch 26/150
3/3 [==============================] - 0s 97ms/step - loss: 0.3156 - accuracy: 0.8681 - val_loss: 0.5239 - val_accuracy: 0.8000
Epoch 118/150
3/3 [==============================] - 0s 69ms/step - loss: 0.4897 - accuracy: 0.7895 - val_loss: 0.5129 - val_accuracy: 0.8000
Epoch 27/150
3/3 [==============================] - 0s 89ms/step - loss: 0.3380 - accuracy: 0.8734 - val_loss: 0.5242 - val_accuracy: 0.8000
Epoch 119/150
3/3 [==============================] - 0s 96ms/step - loss: 0.4518 - accuracy: 0.8132 - val_loss: 0.5111 - val_accuracy: 0.8000
Epoch 28/150
3/3 [==============================] - 0s 101ms/step - loss: 0.3231 - accuracy: 0.8654 - val_loss: 0.5246 - val_accuracy: 0.8105
Epoch 120/150
3/3 [==============================] - 0s 96ms/step - loss: 0.4500 - accuracy: 0.8184 - val_loss: 0.5090 - val_accuracy: 0.8000
Epoch 29/150
3/3 [==============================] - 0s 99ms/step - loss: 0.3365 - accuracy: 0.8496 - val_loss: 0.5245 - val_accuracy: 0.8105
Epoch 121/150
3/3 [==============================] - 0s 105ms/step - loss: 0.4408 - accuracy: 0.8053 - val_loss: 0.5072 - val_accuracy: 0.8000
Epoch 30/150
3/3 [==============================] - 0s 126ms/step - loss: 0.3408 - accuracy: 0.8522 - val_loss: 0.5252 - val_accuracy: 0.8105
Epoch 122/150
3/3 [==============================] - 0s 101ms/step - loss: 0.4572 - accuracy: 0.8237 - val_loss: 0.5052 - val_accuracy: 0.8000
Epoch 31/150
3/3 [==============================] - 0s 99ms/step - loss: 0.3548 - accuracy: 0.8681 - val_loss: 0.5259 - val_accuracy: 0.8105
Epoch 123/150
3/3 [==============================] - 0s 86ms/step - loss: 0.4661 - accuracy: 0.8053 - val_loss: 0.5033 - val_accuracy: 0.8000
Epoch 32/150
3/3 [==============================] - 0s 90ms/step - loss: 0.3272 - accuracy: 0.8707 - val_loss: 0.5265 - val_accuracy: 0.8105
Epoch 124/150
3/3 [==============================] - 0s 97ms/step - loss: 0.4595 - accuracy: 0.8053 - val_loss: 0.5017 - val_accuracy: 0.7895
Epoch 33/150
3/3 [==============================] - 0s 110ms/step - loss: 0.3346 - accuracy: 0.8496 - val_loss: 0.5272 - val_accuracy: 0.8105
Epoch 125/150
3/3 [==============================] - 0s 99ms/step - loss: 0.4604 - accuracy: 0.7947 - val_loss: 0.5007 - val_accuracy: 0.8000
Epoch 34/150
3/3 [==============================] - 0s 108ms/step - loss: 0.3495 - accuracy: 0.8549 - val_loss: 0.5274 - val_accuracy: 0.8105
Epoch 126/150
3/3 [==============================] - 0s 110ms/step - loss: 0.4613 - accuracy: 0.7921 - val_loss: 0.4996 - val_accuracy: 0.7895
Epoch 35/150
3/3 [==============================] - 0s 102ms/step - loss: 0.3256 - accuracy: 0.8654 - val_loss: 0.5279 - val_accuracy: 0.8105
Epoch 127/150
3/3 [==============================] - 0s 86ms/step - loss: 0.4592 - accuracy: 0.8000 - val_loss: 0.4981 - val_accuracy: 0.7895
Epoch 36/150
3/3 [==============================] - 0s 80ms/step - loss: 0.4800 - accuracy: 0.7947 - val_loss: 0.4962 - val_accuracy: 0.7895
Epoch 37/150
3/3 [==============================] - 0s 108ms/step - loss: 0.3320 - accuracy: 0.8760 - val_loss: 0.5283 - val_accuracy: 0.8105
Epoch 128/150
3/3 [==============================] - 0s 106ms/step - loss: 0.4652 - accuracy: 0.8079 - val_loss: 0.4945 - val_accuracy: 0.7895
Epoch 38/150
3/3 [==============================] - 0s 105ms/step - loss: 0.3630 - accuracy: 0.8496 - val_loss: 0.5284 - val_accuracy: 0.8105
Epoch 129/150
3/3 [==============================] - 0s 64ms/step - loss: 0.3303 - accuracy: 0.8654 - val_loss: 0.5283 - val_accuracy: 0.8105
Epoch 130/150
3/3 [==============================] - 0s 94ms/step - loss: 0.4496 - accuracy: 0.8026 - val_loss: 0.4929 - val_accuracy: 0.7895
Epoch 39/150
3/3 [==============================] - 0s 88ms/step - loss: 0.3022 - accuracy: 0.8945 - val_loss: 0.5284 - val_accuracy: 0.8105
Epoch 131/150
3/3 [==============================] - 0s 81ms/step - loss: 0.4650 - accuracy: 0.7974 - val_loss: 0.4914 - val_accuracy: 0.7895
1/3 [=========>....................] - ETA: 0s - loss: 0.2983 - accuracy: 0.8828Epoch 40/150
3/3 [==============================] - 0s 107ms/step - loss: 0.3306 - accuracy: 0.8707 - val_loss: 0.5288 - val_accuracy: 0.8105
Epoch 132/150
3/3 [==============================] - 0s 99ms/step - loss: 0.4704 - accuracy: 0.7921 - val_loss: 0.4896 - val_accuracy: 0.7895
Epoch 41/150
3/3 [==============================] - 0s 82ms/step - loss: 0.4508 - accuracy: 0.8026 - val_loss: 0.4878 - val_accuracy: 0.7895
Epoch 42/150
3/3 [==============================] - 0s 91ms/step - loss: 0.3318 - accuracy: 0.8813 - val_loss: 0.5285 - val_accuracy: 0.8105
Epoch 133/150
3/3 [==============================] - 0s 79ms/step - loss: 0.4898 - accuracy: 0.8000 - val_loss: 0.4861 - val_accuracy: 0.8000
Epoch 43/150
3/3 [==============================] - 0s 79ms/step - loss: 0.3090 - accuracy: 0.8575 - val_loss: 0.5285 - val_accuracy: 0.8105
Epoch 134/150
3/3 [==============================] - 0s 73ms/step - loss: 0.3480 - accuracy: 0.8681 - val_loss: 0.5291 - val_accuracy: 0.8105
Epoch 135/150
3/3 [==============================] - 0s 83ms/step - loss: 0.4567 - accuracy: 0.8158 - val_loss: 0.4844 - val_accuracy: 0.8000
Epoch 44/150
3/3 [==============================] - 0s 74ms/step - loss: 0.4449 - accuracy: 0.8237 - val_loss: 0.4827 - val_accuracy: 0.8000
Epoch 45/150
3/3 [==============================] - 0s 80ms/step - loss: 0.3287 - accuracy: 0.8575 - val_loss: 0.5300 - val_accuracy: 0.8105
Epoch 136/150
3/3 [==============================] - 0s 84ms/step - loss: 0.4801 - accuracy: 0.8211 - val_loss: 0.4816 - val_accuracy: 0.8000
Epoch 46/150
3/3 [==============================] - 0s 86ms/step - loss: 0.3539 - accuracy: 0.8602 - val_loss: 0.5308 - val_accuracy: 0.8105
Epoch 137/150
3/3 [==============================] - 0s 69ms/step - loss: 0.4259 - accuracy: 0.8079 - val_loss: 0.4804 - val_accuracy: 0.8000
Epoch 47/150
3/3 [==============================] - 0s 82ms/step - loss: 0.3370 - accuracy: 0.8522 - val_loss: 0.5311 - val_accuracy: 0.8105
Epoch 138/150
3/3 [==============================] - 0s 79ms/step - loss: 0.4611 - accuracy: 0.8000 - val_loss: 0.4791 - val_accuracy: 0.8000
Epoch 48/150
3/3 [==============================] - 0s 79ms/step - loss: 0.3466 - accuracy: 0.8602 - val_loss: 0.5312 - val_accuracy: 0.8105
Epoch 139/150
3/3 [==============================] - 0s 84ms/step - loss: 0.4388 - accuracy: 0.8053 - val_loss: 0.4783 - val_accuracy: 0.8000
Epoch 49/150
3/3 [==============================] - 0s 83ms/step - loss: 0.3387 - accuracy: 0.8628 - val_loss: 0.5311 - val_accuracy: 0.8105
Epoch 140/150
3/3 [==============================] - 0s 77ms/step - loss: 0.4727 - accuracy: 0.7895 - val_loss: 0.4781 - val_accuracy: 0.8000
Epoch 50/150
3/3 [==============================] - 0s 84ms/step - loss: 0.3297 - accuracy: 0.8654 - val_loss: 0.5317 - val_accuracy: 0.8105
Epoch 141/150
3/3 [==============================] - 0s 76ms/step - loss: 0.4257 - accuracy: 0.8263 - val_loss: 0.4775 - val_accuracy: 0.8000
Epoch 51/150
3/3 [==============================] - 0s 89ms/step - loss: 0.3288 - accuracy: 0.8654 - val_loss: 0.5325 - val_accuracy: 0.8105
Epoch 142/150
3/3 [==============================] - 0s 71ms/step - loss: 0.4345 - accuracy: 0.8289 - val_loss: 0.4771 - val_accuracy: 0.8000
Epoch 52/150
3/3 [==============================] - 0s 52ms/step - loss: 0.4525 - accuracy: 0.8158 - val_loss: 0.4766 - val_accuracy: 0.8000
Epoch 53/150
3/3 [==============================] - 0s 129ms/step - loss: 0.3003 - accuracy: 0.8654 - val_loss: 0.5334 - val_accuracy: 0.8105
Epoch 143/150
3/3 [==============================] - 0s 79ms/step - loss: 0.4379 - accuracy: 0.8132 - val_loss: 0.4759 - val_accuracy: 0.8000
Epoch 54/150
3/3 [==============================] - 0s 90ms/step - loss: 0.3438 - accuracy: 0.8470 - val_loss: 0.5339 - val_accuracy: 0.8105
Epoch 144/150
3/3 [==============================] - 0s 93ms/step - loss: 0.4417 - accuracy: 0.8079 - val_loss: 0.4754 - val_accuracy: 0.8000
Epoch 55/150
3/3 [==============================] - 0s 78ms/step - loss: 0.3253 - accuracy: 0.8602 - val_loss: 0.5342 - val_accuracy: 0.8105
Epoch 145/150
3/3 [==============================] - 0s 93ms/step - loss: 0.4204 - accuracy: 0.8342 - val_loss: 0.4744 - val_accuracy: 0.8000
Epoch 56/150
3/3 [==============================] - 0s 92ms/step - loss: 0.3369 - accuracy: 0.8707 - val_loss: 0.5348 - val_accuracy: 0.8105
Epoch 146/150
3/3 [==============================] - 0s 67ms/step - loss: 0.4069 - accuracy: 0.8421 - val_loss: 0.4732 - val_accuracy: 0.8000
Epoch 57/150
3/3 [==============================] - 0s 76ms/step - loss: 0.3468 - accuracy: 0.8654 - val_loss: 0.5354 - val_accuracy: 0.8105
Epoch 147/150
3/3 [==============================] - 0s 82ms/step - loss: 0.4402 - accuracy: 0.8026 - val_loss: 0.4717 - val_accuracy: 0.8000
Epoch 58/150
3/3 [==============================] - 0s 95ms/step - loss: 0.2968 - accuracy: 0.8839 - val_loss: 0.5359 - val_accuracy: 0.8105
Epoch 148/150
3/3 [==============================] - 0s 76ms/step - loss: 0.4109 - accuracy: 0.8158 - val_loss: 0.4703 - val_accuracy: 0.8000
Epoch 59/150
3/3 [==============================] - 0s 87ms/step - loss: 0.3071 - accuracy: 0.8865 - val_loss: 0.5365 - val_accuracy: 0.8105
Epoch 149/150
3/3 [==============================] - 0s 82ms/step - loss: 0.4122 - accuracy: 0.8263 - val_loss: 0.4693 - val_accuracy: 0.8000
Epoch 60/150
3/3 [==============================] - 0s 87ms/step - loss: 0.3100 - accuracy: 0.8734 - val_loss: 0.5376 - val_accuracy: 0.8105
Epoch 150/150
3/3 [==============================] - 0s 65ms/step - loss: 0.4216 - accuracy: 0.8289 - val_loss: 0.4682 - val_accuracy: 0.8000
Epoch 61/150
3/3 [==============================] - 0s 66ms/step - loss: 0.4177 - accuracy: 0.8263 - val_loss: 0.4668 - val_accuracy: 0.8000
Epoch 62/150
3/3 [==============================] - 0s 84ms/step - loss: 0.3354 - accuracy: 0.8549 - val_loss: 0.5385 - val_accuracy: 0.8105
3/3 [==============================] - 0s 72ms/step - loss: 0.4231 - accuracy: 0.8289 - val_loss: 0.4654 - val_accuracy: 0.8000
Epoch 63/150
3/3 [==============================] - 0s 68ms/step - loss: 0.4306 - accuracy: 0.8184 - val_loss: 0.4647 - val_accuracy: 0.8000
Epoch 64/150
2/2 [==============================] - 0s 12ms/step - loss: 0.5726 - accuracy: 0.7689
3/3 [==============================] - 0s 65ms/step - loss: 0.4322 - accuracy: 0.8105 - val_loss: 0.4639 - val_accuracy: 0.8000
Epoch 65/150
3/3 [==============================] - 0s 73ms/step - loss: 0.3951 - accuracy: 0.8237 - val_loss: 0.4634 - val_accuracy: 0.8000
Epoch 66/150
3/3 [==============================] - 0s 77ms/step - loss: 0.4497 - accuracy: 0.8132 - val_loss: 0.4625 - val_accuracy: 0.8000
Epoch 67/150
3/3 [==============================] - 0s 64ms/step - loss: 0.4226 - accuracy: 0.8237 - val_loss: 0.4622 - val_accuracy: 0.8000
Epoch 68/150
1/3 [=========>....................] - ETA: 0s - loss: 0.3953 - accuracy: 0.8438Epoch 1/150
3/3 [==============================] - 0s 76ms/step - loss: 0.4318 - accuracy: 0.8211 - val_loss: 0.4619 - val_accuracy: 0.8000
Epoch 69/150
3/3 [==============================] - 0s 67ms/step - loss: 0.3792 - accuracy: 0.8526 - val_loss: 0.4617 - val_accuracy: 0.8000
Epoch 70/150
3/3 [==============================] - 0s 76ms/step - loss: 0.3913 - accuracy: 0.8263 - val_loss: 0.4616 - val_accuracy: 0.7895
Epoch 71/150
3/3 [==============================] - 0s 73ms/step - loss: 0.4258 - accuracy: 0.8342 - val_loss: 0.4617 - val_accuracy: 0.7895
Epoch 72/150
3/3 [==============================] - 0s 92ms/step - loss: 0.4567 - accuracy: 0.8342 - val_loss: 0.4614 - val_accuracy: 0.7895
Epoch 73/150
3/3 [==============================] - 0s 77ms/step - loss: 0.4098 - accuracy: 0.8368 - val_loss: 0.4608 - val_accuracy: 0.7895
Epoch 74/150
3/3 [==============================] - 0s 62ms/step - loss: 0.3935 - accuracy: 0.8421 - val_loss: 0.4605 - val_accuracy: 0.8000
Epoch 75/150
3/3 [==============================] - 0s 71ms/step - loss: 0.4022 - accuracy: 0.8395 - val_loss: 0.4604 - val_accuracy: 0.8000
Epoch 76/150
3/3 [==============================] - 0s 102ms/step - loss: 0.4482 - accuracy: 0.8184 - val_loss: 0.4605 - val_accuracy: 0.8000
Epoch 77/150
3/3 [==============================] - 0s 78ms/step - loss: 0.4139 - accuracy: 0.8158 - val_loss: 0.4601 - val_accuracy: 0.8000
Epoch 78/150
3/3 [==============================] - 0s 83ms/step - loss: 0.4272 - accuracy: 0.8184 - val_loss: 0.4599 - val_accuracy: 0.8000
Epoch 79/150
3/3 [==============================] - 0s 87ms/step - loss: 0.4337 - accuracy: 0.8237 - val_loss: 0.4596 - val_accuracy: 0.7895
Epoch 80/150
3/3 [==============================] - 0s 78ms/step - loss: 0.4119 - accuracy: 0.8395 - val_loss: 0.4593 - val_accuracy: 0.7895
Epoch 81/150
3/3 [==============================] - 0s 83ms/step - loss: 0.4070 - accuracy: 0.8184 - val_loss: 0.4585 - val_accuracy: 0.7895
Epoch 82/150
3/3 [==============================] - 0s 88ms/step - loss: 0.4574 - accuracy: 0.8105 - val_loss: 0.4580 - val_accuracy: 0.7895
Epoch 83/150
3/3 [==============================] - 0s 70ms/step - loss: 0.4451 - accuracy: 0.8053 - val_loss: 0.4580 - val_accuracy: 0.7895
Epoch 84/150
3/3 [==============================] - 0s 78ms/step - loss: 0.4044 - accuracy: 0.8211 - val_loss: 0.4577 - val_accuracy: 0.7895
Epoch 85/150
3/3 [==============================] - 0s 50ms/step - loss: 0.4118 - accuracy: 0.8395 - val_loss: 0.4574 - val_accuracy: 0.7895
Epoch 86/150
3/3 [==============================] - 0s 69ms/step - loss: 0.4448 - accuracy: 0.8289 - val_loss: 0.4573 - val_accuracy: 0.7895
Epoch 87/150
3/3 [==============================] - 0s 72ms/step - loss: 0.4250 - accuracy: 0.8105 - val_loss: 0.4579 - val_accuracy: 0.7895
Epoch 88/150
3/3 [==============================] - 0s 92ms/step - loss: 0.4022 - accuracy: 0.8342 - val_loss: 0.4581 - val_accuracy: 0.7895
Epoch 89/150
3/3 [==============================] - 0s 80ms/step - loss: 0.4107 - accuracy: 0.8316 - val_loss: 0.4587 - val_accuracy: 0.7895
Epoch 90/150
3/3 [==============================] - 0s 82ms/step - loss: 0.3940 - accuracy: 0.8342 - val_loss: 0.4590 - val_accuracy: 0.7895
Epoch 91/150
3/3 [==============================] - 0s 84ms/step - loss: 0.3874 - accuracy: 0.8526 - val_loss: 0.4591 - val_accuracy: 0.7895
Epoch 92/150
3/3 [==============================] - 0s 80ms/step - loss: 0.4106 - accuracy: 0.8421 - val_loss: 0.4592 - val_accuracy: 0.7895
Epoch 93/150
3/3 [==============================] - 0s 79ms/step - loss: 0.4028 - accuracy: 0.8263 - val_loss: 0.4592 - val_accuracy: 0.7895
Epoch 94/150
3/3 [==============================] - 0s 119ms/step - loss: 0.4199 - accuracy: 0.8289 - val_loss: 0.4592 - val_accuracy: 0.7895
Epoch 95/150
3/3 [==============================] - 0s 93ms/step - loss: 0.4064 - accuracy: 0.8395 - val_loss: 0.4591 - val_accuracy: 0.7895
Epoch 96/150
3/3 [==============================] - 0s 73ms/step - loss: 0.4012 - accuracy: 0.8368 - val_loss: 0.4593 - val_accuracy: 0.7895
Epoch 97/150
3/3 [==============================] - 0s 90ms/step - loss: 0.3815 - accuracy: 0.8605 - val_loss: 0.4592 - val_accuracy: 0.7895
Epoch 98/150
3/3 [==============================] - 0s 90ms/step - loss: 0.4258 - accuracy: 0.8342 - val_loss: 0.4599 - val_accuracy: 0.7895
Epoch 99/150
3/3 [==============================] - 0s 86ms/step - loss: 0.4218 - accuracy: 0.8289 - val_loss: 0.4602 - val_accuracy: 0.7895
Epoch 100/150
3/3 [==============================] - 0s 68ms/step - loss: 0.4204 - accuracy: 0.8263 - val_loss: 0.4601 - val_accuracy: 0.7895
Epoch 101/150
3/3 [==============================] - 0s 76ms/step - loss: 0.3941 - accuracy: 0.8500 - val_loss: 0.4601 - val_accuracy: 0.7895
Epoch 102/150
3/3 [==============================] - 0s 51ms/step - loss: 0.4180 - accuracy: 0.8579 - val_loss: 0.4603 - val_accuracy: 0.7895
Epoch 103/150
3/3 [==============================] - 0s 87ms/step - loss: 0.3931 - accuracy: 0.8316 - val_loss: 0.4609 - val_accuracy: 0.7895
Epoch 104/150
3/3 [==============================] - 0s 105ms/step - loss: 0.4050 - accuracy: 0.8368 - val_loss: 0.4612 - val_accuracy: 0.7895
Epoch 105/150
3/3 [==============================] - 7s 619ms/step - loss: 0.7666 - accuracy: 0.6079 - val_loss: 0.6662 - val_accuracy: 0.5895
Epoch 2/150
3/3 [==============================] - 0s 90ms/step - loss: 0.3934 - accuracy: 0.8395 - val_loss: 0.4615 - val_accuracy: 0.7895
Epoch 106/150
3/3 [==============================] - 0s 96ms/step - loss: 0.6377 - accuracy: 0.6553 - val_loss: 0.6318 - val_accuracy: 0.6211
Epoch 3/150
3/3 [==============================] - 0s 82ms/step - loss: 0.4234 - accuracy: 0.8053 - val_loss: 0.4619 - val_accuracy: 0.7895
Epoch 107/150
3/3 [==============================] - 0s 98ms/step - loss: 0.6915 - accuracy: 0.6605 - val_loss: 0.6073 - val_accuracy: 0.6421
Epoch 4/150
3/3 [==============================] - 0s 52ms/step - loss: 0.5795 - accuracy: 0.7132 - val_loss: 0.5869 - val_accuracy: 0.7053
Epoch 5/150
3/3 [==============================] - 0s 113ms/step - loss: 0.4082 - accuracy: 0.8474 - val_loss: 0.4621 - val_accuracy: 0.7895
Epoch 108/150
3/3 [==============================] - 0s 98ms/step - loss: 0.5757 - accuracy: 0.7000 - val_loss: 0.5703 - val_accuracy: 0.7579
Epoch 6/150
3/3 [==============================] - 0s 100ms/step - loss: 0.3885 - accuracy: 0.8184 - val_loss: 0.4623 - val_accuracy: 0.7895
Epoch 109/150
3/3 [==============================] - 0s 81ms/step - loss: 0.5323 - accuracy: 0.7421 - val_loss: 0.5556 - val_accuracy: 0.7579
Epoch 7/150
3/3 [==============================] - 0s 97ms/step - loss: 0.3869 - accuracy: 0.8500 - val_loss: 0.4625 - val_accuracy: 0.7895
Epoch 110/150
3/3 [==============================] - 0s 96ms/step - loss: 0.5409 - accuracy: 0.7421 - val_loss: 0.5436 - val_accuracy: 0.7789
Epoch 8/150
3/3 [==============================] - 0s 94ms/step - loss: 0.3900 - accuracy: 0.8474 - val_loss: 0.4628 - val_accuracy: 0.7895
Epoch 111/150
3/3 [==============================] - 0s 98ms/step - loss: 0.5795 - accuracy: 0.7237 - val_loss: 0.5337 - val_accuracy: 0.7895
Epoch 9/150
3/3 [==============================] - 0s 110ms/step - loss: 0.3972 - accuracy: 0.8368 - val_loss: 0.4627 - val_accuracy: 0.7895
Epoch 112/150
3/3 [==============================] - 0s 120ms/step - loss: 0.5161 - accuracy: 0.7553 - val_loss: 0.5246 - val_accuracy: 0.8105
Epoch 10/150
3/3 [==============================] - 0s 124ms/step - loss: 0.4297 - accuracy: 0.8237 - val_loss: 0.4631 - val_accuracy: 0.7895
Epoch 113/150
3/3 [==============================] - 0s 88ms/step - loss: 0.5680 - accuracy: 0.7342 - val_loss: 0.5160 - val_accuracy: 0.8211
Epoch 11/150
3/3 [==============================] - 0s 93ms/step - loss: 0.4203 - accuracy: 0.8368 - val_loss: 0.4637 - val_accuracy: 0.7895
Epoch 114/150
3/3 [==============================] - 0s 90ms/step - loss: 0.5343 - accuracy: 0.7526 - val_loss: 0.5083 - val_accuracy: 0.8421
Epoch 12/150
3/3 [==============================] - 0s 86ms/step - loss: 0.4123 - accuracy: 0.8368 - val_loss: 0.4643 - val_accuracy: 0.7895
Epoch 115/150
3/3 [==============================] - 0s 87ms/step - loss: 0.5189 - accuracy: 0.7737 - val_loss: 0.5019 - val_accuracy: 0.8632
Epoch 13/150
3/3 [==============================] - 0s 82ms/step - loss: 0.3623 - accuracy: 0.8395 - val_loss: 0.4649 - val_accuracy: 0.7895
Epoch 116/150
3/3 [==============================] - 0s 87ms/step - loss: 0.4722 - accuracy: 0.7789 - val_loss: 0.4959 - val_accuracy: 0.8632
Epoch 14/150
3/3 [==============================] - 0s 101ms/step - loss: 0.4036 - accuracy: 0.8237 - val_loss: 0.4650 - val_accuracy: 0.7895
Epoch 117/150
3/3 [==============================] - 0s 89ms/step - loss: 0.5097 - accuracy: 0.7947 - val_loss: 0.4904 - val_accuracy: 0.8632
Epoch 15/150
3/3 [==============================] - 0s 83ms/step - loss: 0.3810 - accuracy: 0.8605 - val_loss: 0.4654 - val_accuracy: 0.7895
Epoch 118/150
3/3 [==============================] - 0s 77ms/step - loss: 0.5130 - accuracy: 0.7526 - val_loss: 0.4854 - val_accuracy: 0.8526
Epoch 16/150
3/3 [==============================] - 0s 82ms/step - loss: 0.3948 - accuracy: 0.8500 - val_loss: 0.4661 - val_accuracy: 0.7895
Epoch 119/150
3/3 [==============================] - 0s 74ms/step - loss: 0.5321 - accuracy: 0.7526 - val_loss: 0.4804 - val_accuracy: 0.8632
Epoch 17/150
3/3 [==============================] - 0s 86ms/step - loss: 0.3667 - accuracy: 0.8684 - val_loss: 0.4665 - val_accuracy: 0.7895
Epoch 120/150
3/3 [==============================] - 0s 73ms/step - loss: 0.4970 - accuracy: 0.7711 - val_loss: 0.4756 - val_accuracy: 0.8632
Epoch 18/150
3/3 [==============================] - 0s 90ms/step - loss: 0.5072 - accuracy: 0.7605 - val_loss: 0.4722 - val_accuracy: 0.8632
Epoch 19/150
3/3 [==============================] - 0s 97ms/step - loss: 0.3801 - accuracy: 0.8658 - val_loss: 0.4670 - val_accuracy: 0.7895
Epoch 121/150
3/3 [==============================] - 0s 68ms/step - loss: 0.3932 - accuracy: 0.8579 - val_loss: 0.4673 - val_accuracy: 0.7895
Epoch 122/150
3/3 [==============================] - 0s 79ms/step - loss: 0.5052 - accuracy: 0.7868 - val_loss: 0.4689 - val_accuracy: 0.8632
Epoch 20/150
3/3 [==============================] - 0s 62ms/step - loss: 0.5136 - accuracy: 0.7526 - val_loss: 0.4657 - val_accuracy: 0.8632
Epoch 21/150
3/3 [==============================] - 0s 69ms/step - loss: 0.3848 - accuracy: 0.8447 - val_loss: 0.4673 - val_accuracy: 0.7895
Epoch 123/150
3/3 [==============================] - 0s 82ms/step - loss: 0.4698 - accuracy: 0.7842 - val_loss: 0.4622 - val_accuracy: 0.8632
Epoch 22/150
3/3 [==============================] - 0s 81ms/step - loss: 0.4247 - accuracy: 0.8184 - val_loss: 0.4676 - val_accuracy: 0.7895
Epoch 124/150
3/3 [==============================] - 0s 86ms/step - loss: 0.4945 - accuracy: 0.7632 - val_loss: 0.4593 - val_accuracy: 0.8632
Epoch 23/150
3/3 [==============================] - 0s 83ms/step - loss: 0.4151 - accuracy: 0.8316 - val_loss: 0.4677 - val_accuracy: 0.7895
Epoch 125/150
3/3 [==============================] - 0s 90ms/step - loss: 0.5099 - accuracy: 0.7895 - val_loss: 0.4561 - val_accuracy: 0.8632
Epoch 24/150
3/3 [==============================] - 0s 98ms/step - loss: 0.3988 - accuracy: 0.8263 - val_loss: 0.4684 - val_accuracy: 0.7895
Epoch 126/150
3/3 [==============================] - 0s 107ms/step - loss: 0.4752 - accuracy: 0.7711 - val_loss: 0.4529 - val_accuracy: 0.8737
Epoch 25/150
3/3 [==============================] - 0s 119ms/step - loss: 0.4120 - accuracy: 0.8368 - val_loss: 0.4686 - val_accuracy: 0.7895
Epoch 127/150
3/3 [==============================] - 0s 109ms/step - loss: 0.4665 - accuracy: 0.7947 - val_loss: 0.4497 - val_accuracy: 0.8737
Epoch 26/150
3/3 [==============================] - 0s 82ms/step - loss: 0.3757 - accuracy: 0.8553 - val_loss: 0.4692 - val_accuracy: 0.7895
Epoch 128/150
3/3 [==============================] - 0s 62ms/step - loss: 0.3993 - accuracy: 0.8421 - val_loss: 0.4695 - val_accuracy: 0.7895
Epoch 129/150
3/3 [==============================] - 0s 85ms/step - loss: 0.4877 - accuracy: 0.7763 - val_loss: 0.4467 - val_accuracy: 0.8737
Epoch 27/150
3/3 [==============================] - 0s 86ms/step - loss: 0.3790 - accuracy: 0.8474 - val_loss: 0.4697 - val_accuracy: 0.7895
Epoch 130/150
3/3 [==============================] - 0s 84ms/step - loss: 0.4745 - accuracy: 0.8079 - val_loss: 0.4438 - val_accuracy: 0.8737
Epoch 28/150
3/3 [==============================] - 0s 66ms/step - loss: 0.4830 - accuracy: 0.7789 - val_loss: 0.4411 - val_accuracy: 0.8737
Epoch 29/150
3/3 [==============================] - 0s 108ms/step - loss: 0.3803 - accuracy: 0.8447 - val_loss: 0.4705 - val_accuracy: 0.7895
Epoch 131/150
3/3 [==============================] - 0s 93ms/step - loss: 0.4834 - accuracy: 0.8026 - val_loss: 0.4380 - val_accuracy: 0.8632
Epoch 30/150
3/3 [==============================] - 0s 87ms/step - loss: 0.3938 - accuracy: 0.8605 - val_loss: 0.4709 - val_accuracy: 0.7895
Epoch 132/150
3/3 [==============================] - 0s 84ms/step - loss: 0.4609 - accuracy: 0.7868 - val_loss: 0.4353 - val_accuracy: 0.8632
Epoch 31/150
3/3 [==============================] - 0s 97ms/step - loss: 0.3976 - accuracy: 0.8474 - val_loss: 0.4706 - val_accuracy: 0.7895
Epoch 133/150
3/3 [==============================] - 0s 109ms/step - loss: 0.4544 - accuracy: 0.8158 - val_loss: 0.4332 - val_accuracy: 0.8632
Epoch 32/150
3/3 [==============================] - 0s 107ms/step - loss: 0.4067 - accuracy: 0.8500 - val_loss: 0.4708 - val_accuracy: 0.7895
Epoch 134/150
3/3 [==============================] - 0s 80ms/step - loss: 0.3886 - accuracy: 0.8632 - val_loss: 0.4709 - val_accuracy: 0.7895
Epoch 135/150
3/3 [==============================] - 0s 140ms/step - loss: 0.4659 - accuracy: 0.8000 - val_loss: 0.4307 - val_accuracy: 0.8632
Epoch 33/150
3/3 [==============================] - 0s 84ms/step - loss: 0.4118 - accuracy: 0.8342 - val_loss: 0.4709 - val_accuracy: 0.7895
Epoch 136/150
3/3 [==============================] - 0s 94ms/step - loss: 0.4514 - accuracy: 0.7895 - val_loss: 0.4285 - val_accuracy: 0.8632
Epoch 34/150
3/3 [==============================] - 0s 92ms/step - loss: 0.3814 - accuracy: 0.8368 - val_loss: 0.4703 - val_accuracy: 0.7895
Epoch 137/150
3/3 [==============================] - 0s 95ms/step - loss: 0.4559 - accuracy: 0.7789 - val_loss: 0.4264 - val_accuracy: 0.8632
Epoch 35/150
3/3 [==============================] - 0s 68ms/step - loss: 0.4215 - accuracy: 0.8395 - val_loss: 0.4698 - val_accuracy: 0.7789
Epoch 138/150
3/3 [==============================] - 0s 78ms/step - loss: 0.4511 - accuracy: 0.7921 - val_loss: 0.4247 - val_accuracy: 0.8526
Epoch 36/150
3/3 [==============================] - 0s 83ms/step - loss: 0.3970 - accuracy: 0.8605 - val_loss: 0.4693 - val_accuracy: 0.7789
Epoch 139/150
3/3 [==============================] - 0s 74ms/step - loss: 0.4470 - accuracy: 0.8000 - val_loss: 0.4226 - val_accuracy: 0.8526
Epoch 37/150
3/3 [==============================] - 0s 63ms/step - loss: 0.3941 - accuracy: 0.8395 - val_loss: 0.4691 - val_accuracy: 0.7789
Epoch 140/150
3/3 [==============================] - 0s 71ms/step - loss: 0.4154 - accuracy: 0.8026 - val_loss: 0.4205 - val_accuracy: 0.8632
Epoch 38/150
3/3 [==============================] - 0s 71ms/step - loss: 0.4080 - accuracy: 0.8132 - val_loss: 0.4690 - val_accuracy: 0.7789
Epoch 141/150
3/3 [==============================] - 0s 89ms/step - loss: 0.4767 - accuracy: 0.7947 - val_loss: 0.4185 - val_accuracy: 0.8632
Epoch 39/150
3/3 [==============================] - 0s 81ms/step - loss: 0.3901 - accuracy: 0.8447 - val_loss: 0.4692 - val_accuracy: 0.7789
Epoch 142/150
3/3 [==============================] - 0s 83ms/step - loss: 0.4503 - accuracy: 0.7868 - val_loss: 0.4165 - val_accuracy: 0.8526
Epoch 40/150
3/3 [==============================] - 0s 81ms/step - loss: 0.3684 - accuracy: 0.8579 - val_loss: 0.4695 - val_accuracy: 0.7789
Epoch 143/150
3/3 [==============================] - 0s 78ms/step - loss: 0.5119 - accuracy: 0.7684 - val_loss: 0.4141 - val_accuracy: 0.8526
Epoch 41/150
3/3 [==============================] - 0s 87ms/step - loss: 0.4158 - accuracy: 0.8105 - val_loss: 0.4696 - val_accuracy: 0.7789
Epoch 144/150
3/3 [==============================] - 0s 85ms/step - loss: 0.4390 - accuracy: 0.8237 - val_loss: 0.4122 - val_accuracy: 0.8526
Epoch 42/150
3/3 [==============================] - 0s 82ms/step - loss: 0.3825 - accuracy: 0.8474 - val_loss: 0.4698 - val_accuracy: 0.7789
Epoch 145/150
3/3 [==============================] - 0s 92ms/step - loss: 0.4460 - accuracy: 0.8053 - val_loss: 0.4101 - val_accuracy: 0.8526
Epoch 43/150
3/3 [==============================] - 0s 92ms/step - loss: 0.3796 - accuracy: 0.8395 - val_loss: 0.4700 - val_accuracy: 0.7789
Epoch 146/150
3/3 [==============================] - 0s 77ms/step - loss: 0.4333 - accuracy: 0.7947 - val_loss: 0.4080 - val_accuracy: 0.8526
Epoch 44/150
3/3 [==============================] - 0s 93ms/step - loss: 0.3884 - accuracy: 0.8553 - val_loss: 0.4702 - val_accuracy: 0.7789
Epoch 147/150
3/3 [==============================] - 0s 106ms/step - loss: 0.4374 - accuracy: 0.7947 - val_loss: 0.4064 - val_accuracy: 0.8526
Epoch 45/150
3/3 [==============================] - 0s 88ms/step - loss: 0.3957 - accuracy: 0.8526 - val_loss: 0.4703 - val_accuracy: 0.7789
Epoch 148/150
3/3 [==============================] - 0s 90ms/step - loss: 0.4688 - accuracy: 0.7868 - val_loss: 0.4048 - val_accuracy: 0.8526
Epoch 46/150
3/3 [==============================] - 0s 93ms/step - loss: 0.3678 - accuracy: 0.8632 - val_loss: 0.4704 - val_accuracy: 0.7789
Epoch 149/150
3/3 [==============================] - 0s 91ms/step - loss: 0.4493 - accuracy: 0.8026 - val_loss: 0.4031 - val_accuracy: 0.8526
Epoch 47/150
3/3 [==============================] - 0s 62ms/step - loss: 0.3896 - accuracy: 0.8500 - val_loss: 0.4700 - val_accuracy: 0.7789
1/3 [=========>....................] - ETA: 0s - loss: 0.3787 - accuracy: 0.8516Epoch 150/150
3/3 [==============================] - 0s 76ms/step - loss: 0.4273 - accuracy: 0.8263 - val_loss: 0.4015 - val_accuracy: 0.8526
Epoch 48/150
3/3 [==============================] - 0s 76ms/step - loss: 0.3770 - accuracy: 0.8526 - val_loss: 0.4697 - val_accuracy: 0.7789
3/3 [==============================] - 0s 80ms/step - loss: 0.4442 - accuracy: 0.7895 - val_loss: 0.3999 - val_accuracy: 0.8526
Epoch 49/150
3/3 [==============================] - 0s 62ms/step - loss: 0.4196 - accuracy: 0.7974 - val_loss: 0.3980 - val_accuracy: 0.8526
Epoch 50/150
2/2 [==============================] - 0s 12ms/step - loss: 0.3965 - accuracy: 0.8481
3/3 [==============================] - 0s 37ms/step - loss: 0.4392 - accuracy: 0.7974 - val_loss: 0.3962 - val_accuracy: 0.8632
Epoch 51/150
3/3 [==============================] - 0s 57ms/step - loss: 0.4551 - accuracy: 0.7868 - val_loss: 0.3945 - val_accuracy: 0.8632
Epoch 52/150
3/3 [==============================] - 0s 44ms/step - loss: 0.4406 - accuracy: 0.8000 - val_loss: 0.3925 - val_accuracy: 0.8632
Epoch 53/150
3/3 [==============================] - 0s 52ms/step - loss: 0.4571 - accuracy: 0.7895 - val_loss: 0.3906 - val_accuracy: 0.8632
Epoch 54/150
3/3 [==============================] - 0s 46ms/step - loss: 0.4502 - accuracy: 0.8000 - val_loss: 0.3888 - val_accuracy: 0.8737
Epoch 55/150
3/3 [==============================] - 0s 46ms/step - loss: 0.4297 - accuracy: 0.8184 - val_loss: 0.3869 - val_accuracy: 0.8737
Epoch 56/150
3/3 [==============================] - 0s 50ms/step - loss: 0.4283 - accuracy: 0.8237 - val_loss: 0.3854 - val_accuracy: 0.8737
Epoch 57/150
3/3 [==============================] - 0s 47ms/step - loss: 0.4552 - accuracy: 0.8053 - val_loss: 0.3844 - val_accuracy: 0.8737
Epoch 58/150
3/3 [==============================] - 0s 46ms/step - loss: 0.4441 - accuracy: 0.8132 - val_loss: 0.3832 - val_accuracy: 0.8737
Epoch 59/150
3/3 [==============================] - 0s 39ms/step - loss: 0.4511 - accuracy: 0.7868 - val_loss: 0.3823 - val_accuracy: 0.8737
Epoch 60/150
3/3 [==============================] - 0s 51ms/step - loss: 0.4162 - accuracy: 0.8026 - val_loss: 0.3814 - val_accuracy: 0.8737
Epoch 61/150
3/3 [==============================] - 0s 47ms/step - loss: 0.4392 - accuracy: 0.8158 - val_loss: 0.3804 - val_accuracy: 0.8737
Epoch 62/150
3/3 [==============================] - 0s 47ms/step - loss: 0.4610 - accuracy: 0.8000 - val_loss: 0.3794 - val_accuracy: 0.8737
Epoch 63/150
3/3 [==============================] - 0s 40ms/step - loss: 0.4620 - accuracy: 0.7974 - val_loss: 0.3782 - val_accuracy: 0.8737
Epoch 64/150
3/3 [==============================] - 0s 38ms/step - loss: 0.4483 - accuracy: 0.7868 - val_loss: 0.3767 - val_accuracy: 0.8737
Epoch 65/150
3/3 [==============================] - 0s 52ms/step - loss: 0.4289 - accuracy: 0.8158 - val_loss: 0.3754 - val_accuracy: 0.8737
Epoch 66/150
3/3 [==============================] - 0s 77ms/step - loss: 0.4199 - accuracy: 0.8289 - val_loss: 0.3740 - val_accuracy: 0.8737
Epoch 67/150
3/3 [==============================] - 0s 58ms/step - loss: 0.4377 - accuracy: 0.8053 - val_loss: 0.3731 - val_accuracy: 0.8737
Epoch 68/150
3/3 [==============================] - 0s 69ms/step - loss: 0.4627 - accuracy: 0.8132 - val_loss: 0.3725 - val_accuracy: 0.8737
Epoch 69/150
3/3 [==============================] - 0s 61ms/step - loss: 0.4476 - accuracy: 0.8000 - val_loss: 0.3721 - val_accuracy: 0.8632
Epoch 70/150
3/3 [==============================] - 0s 70ms/step - loss: 0.4232 - accuracy: 0.8105 - val_loss: 0.3718 - val_accuracy: 0.8632
Epoch 71/150
3/3 [==============================] - 0s 55ms/step - loss: 0.4068 - accuracy: 0.8132 - val_loss: 0.3711 - val_accuracy: 0.8632
Epoch 72/150
3/3 [==============================] - 0s 48ms/step - loss: 0.4541 - accuracy: 0.8079 - val_loss: 0.3704 - val_accuracy: 0.8737
Epoch 73/150
3/3 [==============================] - 0s 47ms/step - loss: 0.4144 - accuracy: 0.8053 - val_loss: 0.3700 - val_accuracy: 0.8737
Epoch 74/150
3/3 [==============================] - 0s 40ms/step - loss: 0.4559 - accuracy: 0.8158 - val_loss: 0.3690 - val_accuracy: 0.8737
Epoch 75/150
3/3 [==============================] - 0s 49ms/step - loss: 0.4281 - accuracy: 0.8000 - val_loss: 0.3688 - val_accuracy: 0.8737
Epoch 76/150
3/3 [==============================] - 0s 46ms/step - loss: 0.4170 - accuracy: 0.8105 - val_loss: 0.3685 - val_accuracy: 0.8737
Epoch 77/150
3/3 [==============================] - 0s 46ms/step - loss: 0.4553 - accuracy: 0.7974 - val_loss: 0.3684 - val_accuracy: 0.8632
Epoch 78/150
3/3 [==============================] - 0s 46ms/step - loss: 0.4068 - accuracy: 0.8158 - val_loss: 0.3680 - val_accuracy: 0.8632
Epoch 79/150
3/3 [==============================] - 0s 41ms/step - loss: 0.4478 - accuracy: 0.8000 - val_loss: 0.3678 - val_accuracy: 0.8632
Epoch 80/150
3/3 [==============================] - 0s 40ms/step - loss: 0.3854 - accuracy: 0.8342 - val_loss: 0.3675 - val_accuracy: 0.8632
Epoch 81/150
3/3 [==============================] - 0s 40ms/step - loss: 0.4821 - accuracy: 0.7895 - val_loss: 0.3671 - val_accuracy: 0.8632
Epoch 82/150
3/3 [==============================] - 0s 47ms/step - loss: 0.4431 - accuracy: 0.7947 - val_loss: 0.3660 - val_accuracy: 0.8737
Epoch 83/150
3/3 [==============================] - 0s 42ms/step - loss: 0.4089 - accuracy: 0.8211 - val_loss: 0.3653 - val_accuracy: 0.8737
Epoch 84/150
3/3 [==============================] - 0s 41ms/step - loss: 0.4108 - accuracy: 0.8079 - val_loss: 0.3647 - val_accuracy: 0.8737
Epoch 85/150
3/3 [==============================] - 0s 42ms/step - loss: 0.4262 - accuracy: 0.8105 - val_loss: 0.3642 - val_accuracy: 0.8737
Epoch 86/150
3/3 [==============================] - 0s 43ms/step - loss: 0.4188 - accuracy: 0.8263 - val_loss: 0.3635 - val_accuracy: 0.8737
Epoch 87/150
3/3 [==============================] - 0s 42ms/step - loss: 0.4165 - accuracy: 0.8105 - val_loss: 0.3632 - val_accuracy: 0.8737
Epoch 88/150
3/3 [==============================] - 0s 54ms/step - loss: 0.4209 - accuracy: 0.8000 - val_loss: 0.3627 - val_accuracy: 0.8737
Epoch 89/150
3/3 [==============================] - 0s 47ms/step - loss: 0.4414 - accuracy: 0.8079 - val_loss: 0.3622 - val_accuracy: 0.8737
Epoch 90/150
3/3 [==============================] - 0s 38ms/step - loss: 0.4658 - accuracy: 0.7868 - val_loss: 0.3619 - val_accuracy: 0.8737
Epoch 91/150
3/3 [==============================] - 0s 45ms/step - loss: 0.3906 - accuracy: 0.8237 - val_loss: 0.3615 - val_accuracy: 0.8737
Epoch 92/150
3/3 [==============================] - 0s 47ms/step - loss: 0.4225 - accuracy: 0.8105 - val_loss: 0.3613 - val_accuracy: 0.8737
Epoch 93/150
3/3 [==============================] - 0s 60ms/step - loss: 0.4423 - accuracy: 0.7789 - val_loss: 0.3608 - val_accuracy: 0.8737
Epoch 94/150
3/3 [==============================] - 0s 57ms/step - loss: 0.4637 - accuracy: 0.7974 - val_loss: 0.3608 - val_accuracy: 0.8737
Epoch 95/150
3/3 [==============================] - 0s 45ms/step - loss: 0.3970 - accuracy: 0.8105 - val_loss: 0.3606 - val_accuracy: 0.8737
Epoch 96/150
3/3 [==============================] - 0s 49ms/step - loss: 0.4118 - accuracy: 0.8105 - val_loss: 0.3596 - val_accuracy: 0.8737
Epoch 97/150
3/3 [==============================] - 0s 52ms/step - loss: 0.4439 - accuracy: 0.7947 - val_loss: 0.3589 - val_accuracy: 0.8737
Epoch 98/150
3/3 [==============================] - 0s 41ms/step - loss: 0.3969 - accuracy: 0.8105 - val_loss: 0.3586 - val_accuracy: 0.8737
Epoch 99/150
3/3 [==============================] - 0s 49ms/step - loss: 0.4198 - accuracy: 0.8105 - val_loss: 0.3581 - val_accuracy: 0.8737
Epoch 100/150
3/3 [==============================] - 0s 40ms/step - loss: 0.4155 - accuracy: 0.8105 - val_loss: 0.3580 - val_accuracy: 0.8842
Epoch 101/150
3/3 [==============================] - 0s 32ms/step - loss: 0.4181 - accuracy: 0.8105 - val_loss: 0.3578 - val_accuracy: 0.8842
Epoch 102/150
3/3 [==============================] - 0s 33ms/step - loss: 0.4132 - accuracy: 0.8158 - val_loss: 0.3577 - val_accuracy: 0.8842
Epoch 103/150
3/3 [==============================] - 0s 40ms/step - loss: 0.4157 - accuracy: 0.8026 - val_loss: 0.3574 - val_accuracy: 0.8842
Epoch 104/150
3/3 [==============================] - 0s 38ms/step - loss: 0.4238 - accuracy: 0.8263 - val_loss: 0.3572 - val_accuracy: 0.8842
Epoch 105/150
3/3 [==============================] - 0s 33ms/step - loss: 0.4195 - accuracy: 0.7921 - val_loss: 0.3568 - val_accuracy: 0.8842
Epoch 106/150
3/3 [==============================] - 0s 41ms/step - loss: 0.4252 - accuracy: 0.8105 - val_loss: 0.3566 - val_accuracy: 0.8842
Epoch 107/150
3/3 [==============================] - 0s 40ms/step - loss: 0.4171 - accuracy: 0.8184 - val_loss: 0.3562 - val_accuracy: 0.8842
Epoch 108/150
3/3 [==============================] - 0s 42ms/step - loss: 0.3870 - accuracy: 0.8079 - val_loss: 0.3561 - val_accuracy: 0.8842
Epoch 109/150
3/3 [==============================] - 0s 39ms/step - loss: 0.4265 - accuracy: 0.8158 - val_loss: 0.3560 - val_accuracy: 0.8842
Epoch 110/150
3/3 [==============================] - 0s 45ms/step - loss: 0.4080 - accuracy: 0.8053 - val_loss: 0.3561 - val_accuracy: 0.8842
Epoch 111/150
3/3 [==============================] - 0s 59ms/step - loss: 0.3775 - accuracy: 0.8474 - val_loss: 0.3564 - val_accuracy: 0.8842
Epoch 112/150
3/3 [==============================] - 0s 50ms/step - loss: 0.4472 - accuracy: 0.8000 - val_loss: 0.3565 - val_accuracy: 0.8842
Epoch 113/150
3/3 [==============================] - 0s 52ms/step - loss: 0.3855 - accuracy: 0.8158 - val_loss: 0.3563 - val_accuracy: 0.8842
Epoch 114/150
3/3 [==============================] - 0s 53ms/step - loss: 0.4270 - accuracy: 0.8079 - val_loss: 0.3557 - val_accuracy: 0.8842
Epoch 115/150
3/3 [==============================] - 0s 44ms/step - loss: 0.4272 - accuracy: 0.8342 - val_loss: 0.3549 - val_accuracy: 0.8842
Epoch 116/150
3/3 [==============================] - 0s 44ms/step - loss: 0.4158 - accuracy: 0.8079 - val_loss: 0.3541 - val_accuracy: 0.8842
Epoch 117/150
3/3 [==============================] - 0s 53ms/step - loss: 0.3814 - accuracy: 0.8263 - val_loss: 0.3536 - val_accuracy: 0.8842
Epoch 118/150
3/3 [==============================] - 0s 49ms/step - loss: 0.4020 - accuracy: 0.8395 - val_loss: 0.3534 - val_accuracy: 0.8842
Epoch 119/150
3/3 [==============================] - 0s 56ms/step - loss: 0.4049 - accuracy: 0.8184 - val_loss: 0.3534 - val_accuracy: 0.8842
Epoch 120/150
3/3 [==============================] - 0s 51ms/step - loss: 0.4170 - accuracy: 0.8263 - val_loss: 0.3537 - val_accuracy: 0.8842
Epoch 121/150
3/3 [==============================] - 0s 43ms/step - loss: 0.4072 - accuracy: 0.8105 - val_loss: 0.3538 - val_accuracy: 0.8842
Epoch 122/150
3/3 [==============================] - 0s 52ms/step - loss: 0.3988 - accuracy: 0.8237 - val_loss: 0.3539 - val_accuracy: 0.8842
Epoch 123/150
3/3 [==============================] - 0s 54ms/step - loss: 0.4200 - accuracy: 0.8184 - val_loss: 0.3539 - val_accuracy: 0.8842
Epoch 124/150
3/3 [==============================] - 0s 47ms/step - loss: 0.4026 - accuracy: 0.8211 - val_loss: 0.3541 - val_accuracy: 0.8842
Epoch 125/150
3/3 [==============================] - 0s 58ms/step - loss: 0.4272 - accuracy: 0.8026 - val_loss: 0.3545 - val_accuracy: 0.8842
Epoch 126/150
3/3 [==============================] - 0s 50ms/step - loss: 0.3857 - accuracy: 0.8368 - val_loss: 0.3543 - val_accuracy: 0.8842
Epoch 127/150
3/3 [==============================] - 0s 49ms/step - loss: 0.4218 - accuracy: 0.8184 - val_loss: 0.3542 - val_accuracy: 0.8842
Epoch 128/150
3/3 [==============================] - 0s 59ms/step - loss: 0.4403 - accuracy: 0.7947 - val_loss: 0.3536 - val_accuracy: 0.8842
Epoch 129/150
3/3 [==============================] - 0s 47ms/step - loss: 0.3829 - accuracy: 0.8237 - val_loss: 0.3536 - val_accuracy: 0.8842
Epoch 130/150
3/3 [==============================] - 0s 41ms/step - loss: 0.4387 - accuracy: 0.8105 - val_loss: 0.3532 - val_accuracy: 0.8842
Epoch 131/150
3/3 [==============================] - 0s 47ms/step - loss: 0.4274 - accuracy: 0.8053 - val_loss: 0.3528 - val_accuracy: 0.8842
Epoch 132/150
3/3 [==============================] - 0s 47ms/step - loss: 0.4026 - accuracy: 0.8342 - val_loss: 0.3522 - val_accuracy: 0.8842
Epoch 133/150
3/3 [==============================] - 0s 47ms/step - loss: 0.3839 - accuracy: 0.8237 - val_loss: 0.3520 - val_accuracy: 0.8842
Epoch 134/150
3/3 [==============================] - 0s 44ms/step - loss: 0.4005 - accuracy: 0.8132 - val_loss: 0.3522 - val_accuracy: 0.8842
Epoch 135/150
3/3 [==============================] - 0s 48ms/step - loss: 0.3772 - accuracy: 0.8395 - val_loss: 0.3525 - val_accuracy: 0.8842
Epoch 136/150
3/3 [==============================] - 0s 55ms/step - loss: 0.3991 - accuracy: 0.8263 - val_loss: 0.3523 - val_accuracy: 0.8842
Epoch 137/150
3/3 [==============================] - 0s 43ms/step - loss: 0.4012 - accuracy: 0.8263 - val_loss: 0.3521 - val_accuracy: 0.8737
Epoch 138/150
3/3 [==============================] - 0s 43ms/step - loss: 0.4067 - accuracy: 0.8211 - val_loss: 0.3520 - val_accuracy: 0.8842
Epoch 139/150
3/3 [==============================] - 0s 43ms/step - loss: 0.4319 - accuracy: 0.8211 - val_loss: 0.3523 - val_accuracy: 0.8737
Epoch 140/150
3/3 [==============================] - 0s 44ms/step - loss: 0.3850 - accuracy: 0.8289 - val_loss: 0.3524 - val_accuracy: 0.8737
Epoch 141/150
3/3 [==============================] - 0s 51ms/step - loss: 0.4164 - accuracy: 0.8237 - val_loss: 0.3521 - val_accuracy: 0.8737
Epoch 142/150
3/3 [==============================] - 0s 46ms/step - loss: 0.4310 - accuracy: 0.8237 - val_loss: 0.3518 - val_accuracy: 0.8737
Epoch 143/150
3/3 [==============================] - 0s 43ms/step - loss: 0.3823 - accuracy: 0.8395 - val_loss: 0.3518 - val_accuracy: 0.8737
Epoch 144/150
3/3 [==============================] - 0s 45ms/step - loss: 0.4022 - accuracy: 0.8105 - val_loss: 0.3519 - val_accuracy: 0.8737
Epoch 145/150
3/3 [==============================] - 0s 46ms/step - loss: 0.3999 - accuracy: 0.8158 - val_loss: 0.3520 - val_accuracy: 0.8737
Epoch 146/150
3/3 [==============================] - 0s 64ms/step - loss: 0.3953 - accuracy: 0.8395 - val_loss: 0.3519 - val_accuracy: 0.8737
Epoch 147/150
3/3 [==============================] - 0s 42ms/step - loss: 0.4360 - accuracy: 0.7921 - val_loss: 0.3518 - val_accuracy: 0.8737
Epoch 148/150
3/3 [==============================] - 0s 57ms/step - loss: 0.4118 - accuracy: 0.8079 - val_loss: 0.3519 - val_accuracy: 0.8737
Epoch 149/150
3/3 [==============================] - 0s 50ms/step - loss: 0.4054 - accuracy: 0.8132 - val_loss: 0.3521 - val_accuracy: 0.8737
Epoch 150/150
3/3 [==============================] - 0s 51ms/step - loss: 0.3998 - accuracy: 0.8289 - val_loss: 0.3524 - val_accuracy: 0.8842
2/2 [==============================] - 0s 10ms/step - loss: 0.4255 - accuracy: 0.8312
Epoch 1/150
9/9 [==============================] - 2s 66ms/step - loss: 0.8149 - accuracy: 0.5360 - val_loss: 0.6960 - val_accuracy: 0.5315
Epoch 2/150
9/9 [==============================] - 0s 12ms/step - loss: 0.6294 - accuracy: 0.6643 - val_loss: 0.6367 - val_accuracy: 0.6643
Epoch 3/150
9/9 [==============================] - 0s 11ms/step - loss: 0.5717 - accuracy: 0.7258 - val_loss: 0.5960 - val_accuracy: 0.7483
Epoch 4/150
9/9 [==============================] - 0s 12ms/step - loss: 0.5314 - accuracy: 0.7469 - val_loss: 0.5677 - val_accuracy: 0.7343
Epoch 5/150
9/9 [==============================] - 0s 13ms/step - loss: 0.5308 - accuracy: 0.7592 - val_loss: 0.5463 - val_accuracy: 0.7203
Epoch 6/150
9/9 [==============================] - 0s 12ms/step - loss: 0.5763 - accuracy: 0.7487 - val_loss: 0.5287 - val_accuracy: 0.7413
Epoch 7/150
9/9 [==============================] - 0s 13ms/step - loss: 0.5133 - accuracy: 0.7610 - val_loss: 0.5146 - val_accuracy: 0.7622
Epoch 8/150
9/9 [==============================] - 0s 12ms/step - loss: 0.5214 - accuracy: 0.7821 - val_loss: 0.5031 - val_accuracy: 0.7622
Epoch 9/150
9/9 [==============================] - 0s 11ms/step - loss: 0.5183 - accuracy: 0.7750 - val_loss: 0.4938 - val_accuracy: 0.7972
Epoch 10/150
9/9 [==============================] - 0s 14ms/step - loss: 0.5416 - accuracy: 0.7803 - val_loss: 0.4851 - val_accuracy: 0.8112
Epoch 11/150
9/9 [==============================] - 0s 12ms/step - loss: 0.5048 - accuracy: 0.7821 - val_loss: 0.4764 - val_accuracy: 0.8112
Epoch 12/150
9/9 [==============================] - 0s 12ms/step - loss: 0.4494 - accuracy: 0.7891 - val_loss: 0.4692 - val_accuracy: 0.8252
Epoch 13/150
9/9 [==============================] - 0s 14ms/step - loss: 0.4768 - accuracy: 0.7873 - val_loss: 0.4634 - val_accuracy: 0.8182
Epoch 14/150
9/9 [==============================] - 0s 13ms/step - loss: 0.4620 - accuracy: 0.7961 - val_loss: 0.4574 - val_accuracy: 0.8252
Epoch 15/150
9/9 [==============================] - 0s 12ms/step - loss: 0.4538 - accuracy: 0.8014 - val_loss: 0.4525 - val_accuracy: 0.8182
Epoch 16/150
9/9 [==============================] - 0s 13ms/step - loss: 0.4737 - accuracy: 0.7996 - val_loss: 0.4478 - val_accuracy: 0.8322
Epoch 17/150
9/9 [==============================] - 0s 14ms/step - loss: 0.4821 - accuracy: 0.8032 - val_loss: 0.4436 - val_accuracy: 0.8252
Epoch 18/150
9/9 [==============================] - 0s 12ms/step - loss: 0.4531 - accuracy: 0.8014 - val_loss: 0.4401 - val_accuracy: 0.8252
Epoch 19/150
9/9 [==============================] - 0s 17ms/step - loss: 0.4633 - accuracy: 0.7821 - val_loss: 0.4377 - val_accuracy: 0.8252
Epoch 20/150
9/9 [==============================] - 0s 18ms/step - loss: 0.4641 - accuracy: 0.7944 - val_loss: 0.4348 - val_accuracy: 0.8322
Epoch 21/150
9/9 [==============================] - 0s 15ms/step - loss: 0.4616 - accuracy: 0.7944 - val_loss: 0.4323 - val_accuracy: 0.8322
Epoch 22/150
9/9 [==============================] - 0s 20ms/step - loss: 0.4340 - accuracy: 0.8032 - val_loss: 0.4298 - val_accuracy: 0.8322
Epoch 23/150
9/9 [==============================] - 0s 15ms/step - loss: 0.4487 - accuracy: 0.8067 - val_loss: 0.4280 - val_accuracy: 0.8322
Epoch 24/150
9/9 [==============================] - 0s 15ms/step - loss: 0.4550 - accuracy: 0.8084 - val_loss: 0.4275 - val_accuracy: 0.8322
Epoch 25/150
9/9 [==============================] - 0s 14ms/step - loss: 0.4849 - accuracy: 0.7961 - val_loss: 0.4267 - val_accuracy: 0.8252
Epoch 26/150
9/9 [==============================] - 0s 14ms/step - loss: 0.4682 - accuracy: 0.7891 - val_loss: 0.4264 - val_accuracy: 0.8252
Epoch 27/150
9/9 [==============================] - 0s 14ms/step - loss: 0.4526 - accuracy: 0.8172 - val_loss: 0.4252 - val_accuracy: 0.8252
Epoch 28/150
9/9 [==============================] - 0s 15ms/step - loss: 0.4372 - accuracy: 0.8225 - val_loss: 0.4240 - val_accuracy: 0.8322
Epoch 29/150
9/9 [==============================] - 0s 17ms/step - loss: 0.4434 - accuracy: 0.8225 - val_loss: 0.4228 - val_accuracy: 0.8322
Epoch 30/150
9/9 [==============================] - 0s 15ms/step - loss: 0.4334 - accuracy: 0.8155 - val_loss: 0.4218 - val_accuracy: 0.8322
Epoch 31/150
9/9 [==============================] - 0s 20ms/step - loss: 0.4216 - accuracy: 0.8313 - val_loss: 0.4209 - val_accuracy: 0.8322
Epoch 32/150
9/9 [==============================] - 0s 20ms/step - loss: 0.4431 - accuracy: 0.8067 - val_loss: 0.4210 - val_accuracy: 0.8322
Epoch 33/150
9/9 [==============================] - 0s 20ms/step - loss: 0.4558 - accuracy: 0.8120 - val_loss: 0.4210 - val_accuracy: 0.8322
Epoch 34/150
9/9 [==============================] - 0s 19ms/step - loss: 0.4456 - accuracy: 0.8067 - val_loss: 0.4224 - val_accuracy: 0.8322
Epoch 35/150
9/9 [==============================] - 0s 18ms/step - loss: 0.4327 - accuracy: 0.8014 - val_loss: 0.4219 - val_accuracy: 0.8322
Epoch 36/150
9/9 [==============================] - 0s 18ms/step - loss: 0.4576 - accuracy: 0.7996 - val_loss: 0.4220 - val_accuracy: 0.8322
Epoch 37/150
9/9 [==============================] - 0s 18ms/step - loss: 0.4143 - accuracy: 0.8190 - val_loss: 0.4214 - val_accuracy: 0.8322
Epoch 38/150
9/9 [==============================] - 0s 19ms/step - loss: 0.4171 - accuracy: 0.8260 - val_loss: 0.4207 - val_accuracy: 0.8322
Epoch 39/150
9/9 [==============================] - 0s 18ms/step - loss: 0.4259 - accuracy: 0.8225 - val_loss: 0.4195 - val_accuracy: 0.8392
Epoch 40/150
9/9 [==============================] - 0s 19ms/step - loss: 0.4323 - accuracy: 0.8401 - val_loss: 0.4189 - val_accuracy: 0.8392
Epoch 41/150
9/9 [==============================] - 0s 19ms/step - loss: 0.4278 - accuracy: 0.8172 - val_loss: 0.4183 - val_accuracy: 0.8392
Epoch 42/150
9/9 [==============================] - 0s 21ms/step - loss: 0.4365 - accuracy: 0.8243 - val_loss: 0.4179 - val_accuracy: 0.8322
Epoch 43/150
9/9 [==============================] - 0s 19ms/step - loss: 0.4473 - accuracy: 0.8084 - val_loss: 0.4187 - val_accuracy: 0.8322
Epoch 44/150
9/9 [==============================] - 0s 19ms/step - loss: 0.4196 - accuracy: 0.8418 - val_loss: 0.4200 - val_accuracy: 0.8322
Epoch 45/150
9/9 [==============================] - 0s 24ms/step - loss: 0.4186 - accuracy: 0.8172 - val_loss: 0.4212 - val_accuracy: 0.8322
Epoch 46/150
9/9 [==============================] - 0s 17ms/step - loss: 0.4324 - accuracy: 0.8278 - val_loss: 0.4227 - val_accuracy: 0.8322
Epoch 47/150
9/9 [==============================] - 0s 18ms/step - loss: 0.4072 - accuracy: 0.8225 - val_loss: 0.4223 - val_accuracy: 0.8322
Epoch 48/150
9/9 [==============================] - 0s 15ms/step - loss: 0.4310 - accuracy: 0.8172 - val_loss: 0.4213 - val_accuracy: 0.8322
Epoch 49/150
9/9 [==============================] - 0s 17ms/step - loss: 0.4164 - accuracy: 0.8278 - val_loss: 0.4215 - val_accuracy: 0.8322
Epoch 50/150
9/9 [==============================] - 0s 18ms/step - loss: 0.4328 - accuracy: 0.8225 - val_loss: 0.4216 - val_accuracy: 0.8322
Epoch 51/150
9/9 [==============================] - 0s 17ms/step - loss: 0.4088 - accuracy: 0.8401 - val_loss: 0.4213 - val_accuracy: 0.8322
Epoch 52/150
9/9 [==============================] - 0s 19ms/step - loss: 0.4134 - accuracy: 0.8348 - val_loss: 0.4187 - val_accuracy: 0.8322
Epoch 53/150
9/9 [==============================] - 0s 19ms/step - loss: 0.4137 - accuracy: 0.8207 - val_loss: 0.4181 - val_accuracy: 0.8322
Epoch 54/150
9/9 [==============================] - 0s 16ms/step - loss: 0.4431 - accuracy: 0.8243 - val_loss: 0.4170 - val_accuracy: 0.8182
Epoch 55/150
9/9 [==============================] - 0s 17ms/step - loss: 0.4214 - accuracy: 0.8243 - val_loss: 0.4176 - val_accuracy: 0.8182
Epoch 56/150
9/9 [==============================] - 0s 15ms/step - loss: 0.3823 - accuracy: 0.8436 - val_loss: 0.4197 - val_accuracy: 0.8322
Epoch 57/150
9/9 [==============================] - 0s 16ms/step - loss: 0.4075 - accuracy: 0.8418 - val_loss: 0.4208 - val_accuracy: 0.8322
Epoch 58/150
9/9 [==============================] - 0s 19ms/step - loss: 0.4308 - accuracy: 0.8207 - val_loss: 0.4212 - val_accuracy: 0.8322
Epoch 59/150
9/9 [==============================] - 0s 18ms/step - loss: 0.4385 - accuracy: 0.8172 - val_loss: 0.4223 - val_accuracy: 0.8322
Epoch 60/150
9/9 [==============================] - 0s 17ms/step - loss: 0.4167 - accuracy: 0.8260 - val_loss: 0.4213 - val_accuracy: 0.8322
Epoch 61/150
9/9 [==============================] - 0s 17ms/step - loss: 0.4218 - accuracy: 0.8137 - val_loss: 0.4197 - val_accuracy: 0.8322
Epoch 62/150
9/9 [==============================] - 0s 17ms/step - loss: 0.4345 - accuracy: 0.7979 - val_loss: 0.4194 - val_accuracy: 0.8322
Epoch 63/150
9/9 [==============================] - 0s 15ms/step - loss: 0.3978 - accuracy: 0.8295 - val_loss: 0.4186 - val_accuracy: 0.8322
Epoch 64/150
9/9 [==============================] - 0s 15ms/step - loss: 0.4405 - accuracy: 0.8243 - val_loss: 0.4184 - val_accuracy: 0.8252
Epoch 65/150
9/9 [==============================] - 0s 16ms/step - loss: 0.3988 - accuracy: 0.8330 - val_loss: 0.4184 - val_accuracy: 0.8252
Epoch 66/150
9/9 [==============================] - 0s 16ms/step - loss: 0.4398 - accuracy: 0.8190 - val_loss: 0.4187 - val_accuracy: 0.8252
Epoch 67/150
9/9 [==============================] - 0s 16ms/step - loss: 0.4021 - accuracy: 0.8401 - val_loss: 0.4180 - val_accuracy: 0.8252
Epoch 68/150
9/9 [==============================] - 0s 18ms/step - loss: 0.4120 - accuracy: 0.8330 - val_loss: 0.4163 - val_accuracy: 0.8252
Epoch 69/150
9/9 [==============================] - 0s 21ms/step - loss: 0.4027 - accuracy: 0.8348 - val_loss: 0.4160 - val_accuracy: 0.8252
Epoch 70/150
9/9 [==============================] - 0s 21ms/step - loss: 0.4141 - accuracy: 0.8348 - val_loss: 0.4163 - val_accuracy: 0.8252
Epoch 71/150
9/9 [==============================] - 0s 14ms/step - loss: 0.4294 - accuracy: 0.8243 - val_loss: 0.4157 - val_accuracy: 0.8252
Epoch 72/150
9/9 [==============================] - 0s 14ms/step - loss: 0.4121 - accuracy: 0.8190 - val_loss: 0.4170 - val_accuracy: 0.8252
Epoch 73/150
9/9 [==============================] - 0s 13ms/step - loss: 0.4272 - accuracy: 0.8207 - val_loss: 0.4169 - val_accuracy: 0.8252
Epoch 74/150
9/9 [==============================] - 0s 16ms/step - loss: 0.3890 - accuracy: 0.8489 - val_loss: 0.4175 - val_accuracy: 0.8322
Epoch 75/150
9/9 [==============================] - 0s 21ms/step - loss: 0.4036 - accuracy: 0.8313 - val_loss: 0.4174 - val_accuracy: 0.8252
Epoch 76/150
9/9 [==============================] - 0s 14ms/step - loss: 0.4068 - accuracy: 0.8313 - val_loss: 0.4182 - val_accuracy: 0.8322
Epoch 77/150
9/9 [==============================] - 0s 13ms/step - loss: 0.4053 - accuracy: 0.8348 - val_loss: 0.4187 - val_accuracy: 0.8322
Epoch 78/150
9/9 [==============================] - 0s 13ms/step - loss: 0.4113 - accuracy: 0.8190 - val_loss: 0.4197 - val_accuracy: 0.8322
Epoch 79/150
9/9 [==============================] - 0s 12ms/step - loss: 0.3809 - accuracy: 0.8418 - val_loss: 0.4197 - val_accuracy: 0.8322
Epoch 80/150
9/9 [==============================] - 0s 14ms/step - loss: 0.3751 - accuracy: 0.8348 - val_loss: 0.4190 - val_accuracy: 0.8322
Epoch 81/150
9/9 [==============================] - 0s 14ms/step - loss: 0.4316 - accuracy: 0.8260 - val_loss: 0.4178 - val_accuracy: 0.8322
Epoch 82/150
9/9 [==============================] - 0s 13ms/step - loss: 0.4029 - accuracy: 0.8330 - val_loss: 0.4184 - val_accuracy: 0.8322
Epoch 83/150
9/9 [==============================] - 0s 15ms/step - loss: 0.3944 - accuracy: 0.8471 - val_loss: 0.4202 - val_accuracy: 0.8322
Epoch 84/150
9/9 [==============================] - 0s 15ms/step - loss: 0.4088 - accuracy: 0.8348 - val_loss: 0.4199 - val_accuracy: 0.8322
Epoch 85/150
9/9 [==============================] - 0s 19ms/step - loss: 0.4165 - accuracy: 0.8190 - val_loss: 0.4189 - val_accuracy: 0.8322
Epoch 86/150
9/9 [==============================] - 0s 14ms/step - loss: 0.3921 - accuracy: 0.8348 - val_loss: 0.4188 - val_accuracy: 0.8322
Epoch 87/150
9/9 [==============================] - 0s 15ms/step - loss: 0.4117 - accuracy: 0.8207 - val_loss: 0.4186 - val_accuracy: 0.8322
Epoch 88/150
9/9 [==============================] - 0s 17ms/step - loss: 0.4070 - accuracy: 0.8225 - val_loss: 0.4172 - val_accuracy: 0.8322
Epoch 89/150
9/9 [==============================] - 0s 16ms/step - loss: 0.4053 - accuracy: 0.8243 - val_loss: 0.4169 - val_accuracy: 0.8252
Epoch 90/150
9/9 [==============================] - 0s 12ms/step - loss: 0.4079 - accuracy: 0.8260 - val_loss: 0.4173 - val_accuracy: 0.8322
Epoch 91/150
9/9 [==============================] - 0s 15ms/step - loss: 0.3935 - accuracy: 0.8366 - val_loss: 0.4168 - val_accuracy: 0.8322
Epoch 92/150
9/9 [==============================] - 0s 14ms/step - loss: 0.4064 - accuracy: 0.8383 - val_loss: 0.4169 - val_accuracy: 0.8322
Epoch 93/150
9/9 [==============================] - 0s 13ms/step - loss: 0.4047 - accuracy: 0.8348 - val_loss: 0.4176 - val_accuracy: 0.8322
Epoch 94/150
9/9 [==============================] - 0s 13ms/step - loss: 0.4100 - accuracy: 0.8489 - val_loss: 0.4169 - val_accuracy: 0.8252
Epoch 95/150
9/9 [==============================] - 0s 13ms/step - loss: 0.4108 - accuracy: 0.8348 - val_loss: 0.4157 - val_accuracy: 0.8252
Epoch 96/150
9/9 [==============================] - 0s 14ms/step - loss: 0.4079 - accuracy: 0.8383 - val_loss: 0.4151 - val_accuracy: 0.8252
Epoch 97/150
9/9 [==============================] - 0s 12ms/step - loss: 0.3983 - accuracy: 0.8348 - val_loss: 0.4154 - val_accuracy: 0.8252
Epoch 98/150
9/9 [==============================] - 0s 13ms/step - loss: 0.4149 - accuracy: 0.8278 - val_loss: 0.4156 - val_accuracy: 0.8252
Epoch 99/150
9/9 [==============================] - 0s 14ms/step - loss: 0.3814 - accuracy: 0.8366 - val_loss: 0.4153 - val_accuracy: 0.8252
Epoch 100/150
9/9 [==============================] - 0s 15ms/step - loss: 0.3969 - accuracy: 0.8436 - val_loss: 0.4147 - val_accuracy: 0.8252
Epoch 101/150
9/9 [==============================] - 0s 14ms/step - loss: 0.4049 - accuracy: 0.8207 - val_loss: 0.4155 - val_accuracy: 0.8252
Epoch 102/150
9/9 [==============================] - 0s 13ms/step - loss: 0.4028 - accuracy: 0.8120 - val_loss: 0.4171 - val_accuracy: 0.8252
Epoch 103/150
9/9 [==============================] - 0s 13ms/step - loss: 0.3779 - accuracy: 0.8524 - val_loss: 0.4175 - val_accuracy: 0.8322
Epoch 104/150
9/9 [==============================] - 0s 13ms/step - loss: 0.4056 - accuracy: 0.8366 - val_loss: 0.4189 - val_accuracy: 0.8252
Epoch 105/150
9/9 [==============================] - 0s 14ms/step - loss: 0.4236 - accuracy: 0.8260 - val_loss: 0.4198 - val_accuracy: 0.8252
Epoch 106/150
9/9 [==============================] - 0s 13ms/step - loss: 0.4224 - accuracy: 0.8330 - val_loss: 0.4199 - val_accuracy: 0.8252
Epoch 107/150
9/9 [==============================] - 0s 13ms/step - loss: 0.3887 - accuracy: 0.8330 - val_loss: 0.4189 - val_accuracy: 0.8252
Epoch 108/150
9/9 [==============================] - 0s 13ms/step - loss: 0.4142 - accuracy: 0.8348 - val_loss: 0.4175 - val_accuracy: 0.8252
Epoch 109/150
9/9 [==============================] - 0s 14ms/step - loss: 0.4112 - accuracy: 0.8401 - val_loss: 0.4171 - val_accuracy: 0.8252
Epoch 110/150
9/9 [==============================] - 0s 13ms/step - loss: 0.4122 - accuracy: 0.8225 - val_loss: 0.4166 - val_accuracy: 0.8322
Epoch 111/150
9/9 [==============================] - 0s 15ms/step - loss: 0.3797 - accuracy: 0.8471 - val_loss: 0.4159 - val_accuracy: 0.8322
Epoch 112/150
9/9 [==============================] - 0s 12ms/step - loss: 0.3927 - accuracy: 0.8278 - val_loss: 0.4163 - val_accuracy: 0.8322
Epoch 113/150
9/9 [==============================] - 0s 19ms/step - loss: 0.4034 - accuracy: 0.8260 - val_loss: 0.4163 - val_accuracy: 0.8252
Epoch 114/150
9/9 [==============================] - 0s 21ms/step - loss: 0.3932 - accuracy: 0.8366 - val_loss: 0.4149 - val_accuracy: 0.8322
Epoch 115/150
9/9 [==============================] - 0s 20ms/step - loss: 0.3930 - accuracy: 0.8418 - val_loss: 0.4151 - val_accuracy: 0.8322
Epoch 116/150
9/9 [==============================] - 0s 17ms/step - loss: 0.3934 - accuracy: 0.8471 - val_loss: 0.4159 - val_accuracy: 0.8322
Epoch 117/150
9/9 [==============================] - 0s 14ms/step - loss: 0.4071 - accuracy: 0.8278 - val_loss: 0.4164 - val_accuracy: 0.8322
Epoch 118/150
9/9 [==============================] - 0s 15ms/step - loss: 0.3854 - accuracy: 0.8471 - val_loss: 0.4155 - val_accuracy: 0.8322
Epoch 119/150
9/9 [==============================] - 0s 15ms/step - loss: 0.3873 - accuracy: 0.8453 - val_loss: 0.4144 - val_accuracy: 0.8322
Epoch 120/150
9/9 [==============================] - 0s 15ms/step - loss: 0.3980 - accuracy: 0.8401 - val_loss: 0.4148 - val_accuracy: 0.8322
Epoch 121/150
9/9 [==============================] - 0s 15ms/step - loss: 0.3923 - accuracy: 0.8436 - val_loss: 0.4160 - val_accuracy: 0.8322
Epoch 122/150
9/9 [==============================] - 0s 15ms/step - loss: 0.3902 - accuracy: 0.8418 - val_loss: 0.4157 - val_accuracy: 0.8322
Epoch 123/150
9/9 [==============================] - 0s 14ms/step - loss: 0.3942 - accuracy: 0.8524 - val_loss: 0.4153 - val_accuracy: 0.8322
Epoch 124/150
9/9 [==============================] - 0s 15ms/step - loss: 0.3847 - accuracy: 0.8471 - val_loss: 0.4157 - val_accuracy: 0.8322
Epoch 125/150
9/9 [==============================] - 0s 16ms/step - loss: 0.3855 - accuracy: 0.8348 - val_loss: 0.4165 - val_accuracy: 0.8322
Epoch 126/150
9/9 [==============================] - 0s 16ms/step - loss: 0.3888 - accuracy: 0.8295 - val_loss: 0.4165 - val_accuracy: 0.8322
Epoch 127/150
9/9 [==============================] - 0s 15ms/step - loss: 0.3726 - accuracy: 0.8348 - val_loss: 0.4178 - val_accuracy: 0.8252
Epoch 128/150
9/9 [==============================] - 0s 14ms/step - loss: 0.3908 - accuracy: 0.8489 - val_loss: 0.4180 - val_accuracy: 0.8252
Epoch 129/150
9/9 [==============================] - 0s 16ms/step - loss: 0.3880 - accuracy: 0.8348 - val_loss: 0.4185 - val_accuracy: 0.8252
Epoch 130/150
9/9 [==============================] - 0s 14ms/step - loss: 0.4084 - accuracy: 0.8243 - val_loss: 0.4186 - val_accuracy: 0.8252
Epoch 131/150
9/9 [==============================] - 0s 16ms/step - loss: 0.4159 - accuracy: 0.8295 - val_loss: 0.4191 - val_accuracy: 0.8252
Epoch 132/150
9/9 [==============================] - 0s 17ms/step - loss: 0.3877 - accuracy: 0.8383 - val_loss: 0.4199 - val_accuracy: 0.8252
Epoch 133/150
9/9 [==============================] - 0s 14ms/step - loss: 0.3999 - accuracy: 0.8313 - val_loss: 0.4190 - val_accuracy: 0.8252
Epoch 134/150
9/9 [==============================] - 0s 17ms/step - loss: 0.3948 - accuracy: 0.8348 - val_loss: 0.4183 - val_accuracy: 0.8252
Epoch 135/150
9/9 [==============================] - 0s 17ms/step - loss: 0.4067 - accuracy: 0.8313 - val_loss: 0.4187 - val_accuracy: 0.8252
Epoch 136/150
9/9 [==============================] - 0s 17ms/step - loss: 0.3914 - accuracy: 0.8383 - val_loss: 0.4195 - val_accuracy: 0.8252
Epoch 137/150
9/9 [==============================] - 0s 21ms/step - loss: 0.3919 - accuracy: 0.8278 - val_loss: 0.4194 - val_accuracy: 0.8252
Epoch 138/150
9/9 [==============================] - 0s 19ms/step - loss: 0.3998 - accuracy: 0.8401 - val_loss: 0.4200 - val_accuracy: 0.8252
Epoch 139/150
9/9 [==============================] - 0s 20ms/step - loss: 0.3889 - accuracy: 0.8295 - val_loss: 0.4197 - val_accuracy: 0.8252
Epoch 140/150
9/9 [==============================] - 0s 16ms/step - loss: 0.3908 - accuracy: 0.8330 - val_loss: 0.4185 - val_accuracy: 0.8252
Epoch 141/150
9/9 [==============================] - 0s 13ms/step - loss: 0.3955 - accuracy: 0.8506 - val_loss: 0.4199 - val_accuracy: 0.8252
Epoch 142/150
9/9 [==============================] - 0s 11ms/step - loss: 0.3894 - accuracy: 0.8383 - val_loss: 0.4209 - val_accuracy: 0.8252
Epoch 143/150
9/9 [==============================] - 0s 13ms/step - loss: 0.3929 - accuracy: 0.8366 - val_loss: 0.4209 - val_accuracy: 0.8252
Epoch 144/150
9/9 [==============================] - 0s 13ms/step - loss: 0.3751 - accuracy: 0.8524 - val_loss: 0.4193 - val_accuracy: 0.8322
Epoch 145/150
9/9 [==============================] - 0s 14ms/step - loss: 0.3860 - accuracy: 0.8383 - val_loss: 0.4186 - val_accuracy: 0.8322
Epoch 146/150
9/9 [==============================] - 0s 19ms/step - loss: 0.4020 - accuracy: 0.8278 - val_loss: 0.4188 - val_accuracy: 0.8322
Epoch 147/150
9/9 [==============================] - 0s 15ms/step - loss: 0.3784 - accuracy: 0.8401 - val_loss: 0.4177 - val_accuracy: 0.8322
Epoch 148/150
9/9 [==============================] - 0s 20ms/step - loss: 0.3929 - accuracy: 0.8313 - val_loss: 0.4187 - val_accuracy: 0.8322
Epoch 149/150
9/9 [==============================] - 0s 20ms/step - loss: 0.3879 - accuracy: 0.8541 - val_loss: 0.4181 - val_accuracy: 0.8322
Epoch 150/150
9/9 [==============================] - 0s 21ms/step - loss: 0.4142 - accuracy: 0.8172 - val_loss: 0.4178 - val_accuracy: 0.8322
Best: 0.832865 using {'batch_size': 64, 'dropout1': 0.3, 'dropout2': 0.2, 'lr': 0.0007}
Total runtime of the program is 7868.645040512085
dask_model = create_model(batch_size=dask_result.best_params_['batch_size'],
lr=dask_result.best_params_['lr'],
dropout1=dask_result.best_params_['dropout1'],
dropout2=dask_result.best_params_['dropout2'])
optimizer = tf.keras.optimizers.Adamax(dask_result.best_params_['lr'])
dask_model.compile(loss='binary_crossentropy',
optimizer=optimizer,
metrics=['accuracy'])
dask_model.summary()
Model: "sequential"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
dense (Dense) (None, 128) 1920
dropout (Dropout) (None, 128) 0
batch_normalization (BatchN (None, 128) 512
ormalization)
dense_1 (Dense) (None, 64) 8256
dropout_1 (Dropout) (None, 64) 0
batch_normalization_1 (Batc (None, 64) 256
hNormalization)
dense_2 (Dense) (None, 1) 65
=================================================================
Total params: 11,009
Trainable params: 10,625
Non-trainable params: 384
_________________________________________________________________
history_dask = dask_model.fit(x_train_scaled,
y_train,
epochs=150,
batch_size = dask_result.best_params_['batch_size'],
verbose=1,
validation_split=0.2)
Epoch 1/150 9/9 [==============================] - 4s 146ms/step - loss: 0.7529 - accuracy: 0.6397 - val_loss: 0.7019 - val_accuracy: 0.5245 Epoch 2/150 9/9 [==============================] - 0s 38ms/step - loss: 0.5952 - accuracy: 0.7153 - val_loss: 0.6400 - val_accuracy: 0.6713 Epoch 3/150 9/9 [==============================] - 0s 35ms/step - loss: 0.5687 - accuracy: 0.7311 - val_loss: 0.5962 - val_accuracy: 0.7273 Epoch 4/150 9/9 [==============================] - 0s 31ms/step - loss: 0.5195 - accuracy: 0.7487 - val_loss: 0.5679 - val_accuracy: 0.7483 Epoch 5/150 9/9 [==============================] - 0s 23ms/step - loss: 0.5601 - accuracy: 0.7417 - val_loss: 0.5480 - val_accuracy: 0.7692 Epoch 6/150 9/9 [==============================] - 0s 42ms/step - loss: 0.4918 - accuracy: 0.7768 - val_loss: 0.5310 - val_accuracy: 0.7972 Epoch 7/150 9/9 [==============================] - 0s 34ms/step - loss: 0.5149 - accuracy: 0.7540 - val_loss: 0.5162 - val_accuracy: 0.7902 Epoch 8/150 9/9 [==============================] - 0s 27ms/step - loss: 0.4908 - accuracy: 0.7733 - val_loss: 0.5029 - val_accuracy: 0.8042 Epoch 9/150 9/9 [==============================] - 0s 38ms/step - loss: 0.4915 - accuracy: 0.7909 - val_loss: 0.4927 - val_accuracy: 0.7972 Epoch 10/150 9/9 [==============================] - 0s 28ms/step - loss: 0.4960 - accuracy: 0.7944 - val_loss: 0.4840 - val_accuracy: 0.7972 Epoch 11/150 9/9 [==============================] - 0s 35ms/step - loss: 0.4733 - accuracy: 0.7979 - val_loss: 0.4766 - val_accuracy: 0.8112 Epoch 12/150 9/9 [==============================] - 0s 32ms/step - loss: 0.4609 - accuracy: 0.7891 - val_loss: 0.4702 - val_accuracy: 0.8182 Epoch 13/150 9/9 [==============================] - 0s 31ms/step - loss: 0.4792 - accuracy: 0.7891 - val_loss: 0.4640 - val_accuracy: 0.8252 Epoch 14/150 9/9 [==============================] - 0s 30ms/step - loss: 0.4843 - accuracy: 0.8014 - val_loss: 0.4577 - val_accuracy: 0.8252 Epoch 15/150 9/9 [==============================] - 0s 49ms/step - loss: 0.4552 - accuracy: 0.8207 - val_loss: 0.4525 - val_accuracy: 0.8322 Epoch 16/150 9/9 [==============================] - 0s 24ms/step - loss: 0.4943 - accuracy: 0.7856 - val_loss: 0.4471 - val_accuracy: 0.8322 Epoch 17/150 9/9 [==============================] - 0s 36ms/step - loss: 0.4796 - accuracy: 0.7926 - val_loss: 0.4414 - val_accuracy: 0.8322 Epoch 18/150 9/9 [==============================] - 0s 36ms/step - loss: 0.4503 - accuracy: 0.7891 - val_loss: 0.4370 - val_accuracy: 0.8322 Epoch 19/150 9/9 [==============================] - 0s 27ms/step - loss: 0.4732 - accuracy: 0.7891 - val_loss: 0.4337 - val_accuracy: 0.8322 Epoch 20/150 9/9 [==============================] - 0s 33ms/step - loss: 0.4897 - accuracy: 0.7768 - val_loss: 0.4307 - val_accuracy: 0.8392 Epoch 21/150 9/9 [==============================] - 0s 34ms/step - loss: 0.4179 - accuracy: 0.8225 - val_loss: 0.4279 - val_accuracy: 0.8392 Epoch 22/150 9/9 [==============================] - 0s 30ms/step - loss: 0.4502 - accuracy: 0.8067 - val_loss: 0.4246 - val_accuracy: 0.8392 Epoch 23/150 9/9 [==============================] - 0s 31ms/step - loss: 0.4490 - accuracy: 0.8172 - val_loss: 0.4213 - val_accuracy: 0.8322 Epoch 24/150 9/9 [==============================] - 0s 34ms/step - loss: 0.4652 - accuracy: 0.8014 - val_loss: 0.4194 - val_accuracy: 0.8322 Epoch 25/150 9/9 [==============================] - 0s 30ms/step - loss: 0.4634 - accuracy: 0.8084 - val_loss: 0.4177 - val_accuracy: 0.8322 Epoch 26/150 9/9 [==============================] - 0s 30ms/step - loss: 0.4457 - accuracy: 0.8172 - val_loss: 0.4169 - val_accuracy: 0.8322 Epoch 27/150 9/9 [==============================] - 0s 23ms/step - loss: 0.4758 - accuracy: 0.8067 - val_loss: 0.4166 - val_accuracy: 0.8252 Epoch 28/150 9/9 [==============================] - 0s 19ms/step - loss: 0.4152 - accuracy: 0.8190 - val_loss: 0.4154 - val_accuracy: 0.8252 Epoch 29/150 9/9 [==============================] - 0s 18ms/step - loss: 0.4728 - accuracy: 0.8049 - val_loss: 0.4149 - val_accuracy: 0.8252 Epoch 30/150 9/9 [==============================] - 0s 18ms/step - loss: 0.4513 - accuracy: 0.8084 - val_loss: 0.4145 - val_accuracy: 0.8252 Epoch 31/150 9/9 [==============================] - 0s 23ms/step - loss: 0.4402 - accuracy: 0.8155 - val_loss: 0.4140 - val_accuracy: 0.8322 Epoch 32/150 9/9 [==============================] - 0s 19ms/step - loss: 0.4102 - accuracy: 0.8260 - val_loss: 0.4141 - val_accuracy: 0.8252 Epoch 33/150 9/9 [==============================] - 0s 17ms/step - loss: 0.4221 - accuracy: 0.8155 - val_loss: 0.4137 - val_accuracy: 0.8322 Epoch 34/150 9/9 [==============================] - 0s 17ms/step - loss: 0.4287 - accuracy: 0.8330 - val_loss: 0.4147 - val_accuracy: 0.8322 Epoch 35/150 9/9 [==============================] - 0s 19ms/step - loss: 0.4442 - accuracy: 0.8120 - val_loss: 0.4140 - val_accuracy: 0.8322 Epoch 36/150 9/9 [==============================] - 0s 15ms/step - loss: 0.4260 - accuracy: 0.8225 - val_loss: 0.4131 - val_accuracy: 0.8322 Epoch 37/150 9/9 [==============================] - 0s 20ms/step - loss: 0.4455 - accuracy: 0.8313 - val_loss: 0.4127 - val_accuracy: 0.8322 Epoch 38/150 9/9 [==============================] - 0s 11ms/step - loss: 0.4424 - accuracy: 0.8067 - val_loss: 0.4120 - val_accuracy: 0.8392 Epoch 39/150 9/9 [==============================] - 0s 12ms/step - loss: 0.4309 - accuracy: 0.8260 - val_loss: 0.4118 - val_accuracy: 0.8392 Epoch 40/150 9/9 [==============================] - 0s 17ms/step - loss: 0.4595 - accuracy: 0.8190 - val_loss: 0.4122 - val_accuracy: 0.8392 Epoch 41/150 9/9 [==============================] - 0s 18ms/step - loss: 0.4149 - accuracy: 0.8190 - val_loss: 0.4128 - val_accuracy: 0.8392 Epoch 42/150 9/9 [==============================] - 0s 17ms/step - loss: 0.4281 - accuracy: 0.8049 - val_loss: 0.4129 - val_accuracy: 0.8392 Epoch 43/150 9/9 [==============================] - 0s 28ms/step - loss: 0.4193 - accuracy: 0.8260 - val_loss: 0.4130 - val_accuracy: 0.8392 Epoch 44/150 9/9 [==============================] - 0s 19ms/step - loss: 0.4195 - accuracy: 0.8330 - val_loss: 0.4137 - val_accuracy: 0.8392 Epoch 45/150 9/9 [==============================] - 0s 17ms/step - loss: 0.4308 - accuracy: 0.8102 - val_loss: 0.4147 - val_accuracy: 0.8392 Epoch 46/150 9/9 [==============================] - 0s 17ms/step - loss: 0.4176 - accuracy: 0.8243 - val_loss: 0.4160 - val_accuracy: 0.8392 Epoch 47/150 9/9 [==============================] - 0s 17ms/step - loss: 0.4098 - accuracy: 0.8260 - val_loss: 0.4159 - val_accuracy: 0.8322 Epoch 48/150 9/9 [==============================] - 0s 17ms/step - loss: 0.4358 - accuracy: 0.8190 - val_loss: 0.4155 - val_accuracy: 0.8322 Epoch 49/150 9/9 [==============================] - 0s 18ms/step - loss: 0.4269 - accuracy: 0.8295 - val_loss: 0.4151 - val_accuracy: 0.8252 Epoch 50/150 9/9 [==============================] - 0s 20ms/step - loss: 0.4398 - accuracy: 0.8155 - val_loss: 0.4158 - val_accuracy: 0.8182 Epoch 51/150 9/9 [==============================] - 0s 17ms/step - loss: 0.4153 - accuracy: 0.8313 - val_loss: 0.4168 - val_accuracy: 0.8182 Epoch 52/150 9/9 [==============================] - 0s 16ms/step - loss: 0.4215 - accuracy: 0.8172 - val_loss: 0.4167 - val_accuracy: 0.8182 Epoch 53/150 9/9 [==============================] - 0s 19ms/step - loss: 0.4037 - accuracy: 0.8295 - val_loss: 0.4176 - val_accuracy: 0.8252 Epoch 54/150 9/9 [==============================] - 0s 17ms/step - loss: 0.4053 - accuracy: 0.8383 - val_loss: 0.4169 - val_accuracy: 0.8392 Epoch 55/150 9/9 [==============================] - 0s 17ms/step - loss: 0.4220 - accuracy: 0.8278 - val_loss: 0.4169 - val_accuracy: 0.8392 Epoch 56/150 9/9 [==============================] - 0s 15ms/step - loss: 0.4359 - accuracy: 0.8172 - val_loss: 0.4169 - val_accuracy: 0.8322 Epoch 57/150 9/9 [==============================] - 0s 17ms/step - loss: 0.4200 - accuracy: 0.8348 - val_loss: 0.4164 - val_accuracy: 0.8392 Epoch 58/150 9/9 [==============================] - 0s 16ms/step - loss: 0.4208 - accuracy: 0.8243 - val_loss: 0.4159 - val_accuracy: 0.8322 Epoch 59/150 9/9 [==============================] - 0s 17ms/step - loss: 0.4123 - accuracy: 0.8295 - val_loss: 0.4170 - val_accuracy: 0.8322 Epoch 60/150 9/9 [==============================] - 0s 15ms/step - loss: 0.4225 - accuracy: 0.8278 - val_loss: 0.4184 - val_accuracy: 0.8322 Epoch 61/150 9/9 [==============================] - 0s 17ms/step - loss: 0.4237 - accuracy: 0.8207 - val_loss: 0.4184 - val_accuracy: 0.8322 Epoch 62/150 9/9 [==============================] - 0s 19ms/step - loss: 0.4272 - accuracy: 0.8172 - val_loss: 0.4190 - val_accuracy: 0.8322 Epoch 63/150 9/9 [==============================] - 0s 21ms/step - loss: 0.4075 - accuracy: 0.8225 - val_loss: 0.4189 - val_accuracy: 0.8322 Epoch 64/150 9/9 [==============================] - 0s 18ms/step - loss: 0.4177 - accuracy: 0.8260 - val_loss: 0.4188 - val_accuracy: 0.8322 Epoch 65/150 9/9 [==============================] - 0s 17ms/step - loss: 0.4046 - accuracy: 0.8225 - val_loss: 0.4188 - val_accuracy: 0.8322 Epoch 66/150 9/9 [==============================] - 0s 21ms/step - loss: 0.4339 - accuracy: 0.8120 - val_loss: 0.4189 - val_accuracy: 0.8322 Epoch 67/150 9/9 [==============================] - 0s 43ms/step - loss: 0.4177 - accuracy: 0.8172 - val_loss: 0.4192 - val_accuracy: 0.8322 Epoch 68/150 9/9 [==============================] - 0s 48ms/step - loss: 0.3959 - accuracy: 0.8366 - val_loss: 0.4183 - val_accuracy: 0.8322 Epoch 69/150 9/9 [==============================] - 0s 41ms/step - loss: 0.4165 - accuracy: 0.8313 - val_loss: 0.4189 - val_accuracy: 0.8322 Epoch 70/150 9/9 [==============================] - 0s 35ms/step - loss: 0.3829 - accuracy: 0.8366 - val_loss: 0.4192 - val_accuracy: 0.8392 Epoch 71/150 9/9 [==============================] - 0s 42ms/step - loss: 0.4228 - accuracy: 0.8295 - val_loss: 0.4181 - val_accuracy: 0.8392 Epoch 72/150 9/9 [==============================] - 0s 41ms/step - loss: 0.4127 - accuracy: 0.8172 - val_loss: 0.4181 - val_accuracy: 0.8392 Epoch 73/150 9/9 [==============================] - 0s 41ms/step - loss: 0.4062 - accuracy: 0.8330 - val_loss: 0.4166 - val_accuracy: 0.8392 Epoch 74/150 9/9 [==============================] - 0s 35ms/step - loss: 0.4137 - accuracy: 0.8295 - val_loss: 0.4167 - val_accuracy: 0.8392 Epoch 75/150 9/9 [==============================] - 0s 45ms/step - loss: 0.4187 - accuracy: 0.8225 - val_loss: 0.4169 - val_accuracy: 0.8392 Epoch 76/150 9/9 [==============================] - 0s 47ms/step - loss: 0.4034 - accuracy: 0.8366 - val_loss: 0.4173 - val_accuracy: 0.8392 Epoch 77/150 9/9 [==============================] - 0s 26ms/step - loss: 0.4042 - accuracy: 0.8295 - val_loss: 0.4179 - val_accuracy: 0.8392 Epoch 78/150 9/9 [==============================] - 0s 31ms/step - loss: 0.3957 - accuracy: 0.8295 - val_loss: 0.4198 - val_accuracy: 0.8392 Epoch 79/150 9/9 [==============================] - 0s 30ms/step - loss: 0.3748 - accuracy: 0.8471 - val_loss: 0.4205 - val_accuracy: 0.8392 Epoch 80/150 9/9 [==============================] - 0s 37ms/step - loss: 0.4006 - accuracy: 0.8330 - val_loss: 0.4203 - val_accuracy: 0.8392 Epoch 81/150 9/9 [==============================] - 0s 30ms/step - loss: 0.4164 - accuracy: 0.8155 - val_loss: 0.4203 - val_accuracy: 0.8392 Epoch 82/150 9/9 [==============================] - 0s 37ms/step - loss: 0.3970 - accuracy: 0.8366 - val_loss: 0.4197 - val_accuracy: 0.8392 Epoch 83/150 9/9 [==============================] - 0s 20ms/step - loss: 0.4081 - accuracy: 0.8366 - val_loss: 0.4199 - val_accuracy: 0.8392 Epoch 84/150 9/9 [==============================] - 0s 28ms/step - loss: 0.4051 - accuracy: 0.8172 - val_loss: 0.4190 - val_accuracy: 0.8392 Epoch 85/150 9/9 [==============================] - 0s 24ms/step - loss: 0.4143 - accuracy: 0.8330 - val_loss: 0.4190 - val_accuracy: 0.8392 Epoch 86/150 9/9 [==============================] - 0s 21ms/step - loss: 0.4043 - accuracy: 0.8295 - val_loss: 0.4187 - val_accuracy: 0.8392 Epoch 87/150 9/9 [==============================] - 0s 17ms/step - loss: 0.3993 - accuracy: 0.8172 - val_loss: 0.4185 - val_accuracy: 0.8392 Epoch 88/150 9/9 [==============================] - 0s 19ms/step - loss: 0.4083 - accuracy: 0.8383 - val_loss: 0.4172 - val_accuracy: 0.8392 Epoch 89/150 9/9 [==============================] - 0s 21ms/step - loss: 0.4054 - accuracy: 0.8313 - val_loss: 0.4169 - val_accuracy: 0.8392 Epoch 90/150 9/9 [==============================] - 0s 27ms/step - loss: 0.3985 - accuracy: 0.8313 - val_loss: 0.4184 - val_accuracy: 0.8392 Epoch 91/150 9/9 [==============================] - 0s 34ms/step - loss: 0.3980 - accuracy: 0.8330 - val_loss: 0.4193 - val_accuracy: 0.8392 Epoch 92/150 9/9 [==============================] - 0s 32ms/step - loss: 0.3863 - accuracy: 0.8366 - val_loss: 0.4216 - val_accuracy: 0.8392 Epoch 93/150 9/9 [==============================] - 0s 30ms/step - loss: 0.4070 - accuracy: 0.8471 - val_loss: 0.4227 - val_accuracy: 0.8392 Epoch 94/150 9/9 [==============================] - 0s 19ms/step - loss: 0.4123 - accuracy: 0.8383 - val_loss: 0.4230 - val_accuracy: 0.8392 Epoch 95/150 9/9 [==============================] - 0s 26ms/step - loss: 0.4238 - accuracy: 0.8313 - val_loss: 0.4223 - val_accuracy: 0.8392 Epoch 96/150 9/9 [==============================] - 0s 32ms/step - loss: 0.4160 - accuracy: 0.8225 - val_loss: 0.4212 - val_accuracy: 0.8392 Epoch 97/150 9/9 [==============================] - 0s 28ms/step - loss: 0.4151 - accuracy: 0.8348 - val_loss: 0.4219 - val_accuracy: 0.8322 Epoch 98/150 9/9 [==============================] - 0s 32ms/step - loss: 0.3886 - accuracy: 0.8401 - val_loss: 0.4226 - val_accuracy: 0.8322 Epoch 99/150 9/9 [==============================] - 0s 21ms/step - loss: 0.3956 - accuracy: 0.8330 - val_loss: 0.4223 - val_accuracy: 0.8322 Epoch 100/150 9/9 [==============================] - 0s 25ms/step - loss: 0.4108 - accuracy: 0.8243 - val_loss: 0.4215 - val_accuracy: 0.8392 Epoch 101/150 9/9 [==============================] - 0s 25ms/step - loss: 0.3855 - accuracy: 0.8401 - val_loss: 0.4214 - val_accuracy: 0.8392 Epoch 102/150 9/9 [==============================] - 0s 24ms/step - loss: 0.4081 - accuracy: 0.8383 - val_loss: 0.4215 - val_accuracy: 0.8392 Epoch 103/150 9/9 [==============================] - 0s 24ms/step - loss: 0.3847 - accuracy: 0.8559 - val_loss: 0.4219 - val_accuracy: 0.8392 Epoch 104/150 9/9 [==============================] - 0s 25ms/step - loss: 0.4071 - accuracy: 0.8295 - val_loss: 0.4229 - val_accuracy: 0.8392 Epoch 105/150 9/9 [==============================] - 0s 18ms/step - loss: 0.4287 - accuracy: 0.8330 - val_loss: 0.4233 - val_accuracy: 0.8392 Epoch 106/150 9/9 [==============================] - 0s 30ms/step - loss: 0.4051 - accuracy: 0.8383 - val_loss: 0.4240 - val_accuracy: 0.8392 Epoch 107/150 9/9 [==============================] - 0s 30ms/step - loss: 0.3925 - accuracy: 0.8313 - val_loss: 0.4252 - val_accuracy: 0.8322 Epoch 108/150 9/9 [==============================] - 0s 31ms/step - loss: 0.3937 - accuracy: 0.8313 - val_loss: 0.4249 - val_accuracy: 0.8322 Epoch 109/150 9/9 [==============================] - 0s 19ms/step - loss: 0.4280 - accuracy: 0.8243 - val_loss: 0.4233 - val_accuracy: 0.8392 Epoch 110/150 9/9 [==============================] - 0s 19ms/step - loss: 0.3922 - accuracy: 0.8453 - val_loss: 0.4219 - val_accuracy: 0.8392 Epoch 111/150 9/9 [==============================] - 0s 16ms/step - loss: 0.3947 - accuracy: 0.8278 - val_loss: 0.4215 - val_accuracy: 0.8392 Epoch 112/150 9/9 [==============================] - 0s 23ms/step - loss: 0.3885 - accuracy: 0.8401 - val_loss: 0.4226 - val_accuracy: 0.8392 Epoch 113/150 9/9 [==============================] - 0s 23ms/step - loss: 0.4001 - accuracy: 0.8418 - val_loss: 0.4223 - val_accuracy: 0.8392 Epoch 114/150 9/9 [==============================] - 0s 23ms/step - loss: 0.4018 - accuracy: 0.8278 - val_loss: 0.4218 - val_accuracy: 0.8392 Epoch 115/150 9/9 [==============================] - 0s 22ms/step - loss: 0.4029 - accuracy: 0.8313 - val_loss: 0.4217 - val_accuracy: 0.8392 Epoch 116/150 9/9 [==============================] - 0s 25ms/step - loss: 0.4035 - accuracy: 0.8401 - val_loss: 0.4217 - val_accuracy: 0.8392 Epoch 117/150 9/9 [==============================] - 0s 22ms/step - loss: 0.3706 - accuracy: 0.8559 - val_loss: 0.4225 - val_accuracy: 0.8322 Epoch 118/150 9/9 [==============================] - 0s 39ms/step - loss: 0.4033 - accuracy: 0.8330 - val_loss: 0.4227 - val_accuracy: 0.8322 Epoch 119/150 9/9 [==============================] - 0s 39ms/step - loss: 0.3803 - accuracy: 0.8313 - val_loss: 0.4225 - val_accuracy: 0.8322 Epoch 120/150 9/9 [==============================] - 0s 40ms/step - loss: 0.3740 - accuracy: 0.8401 - val_loss: 0.4230 - val_accuracy: 0.8322 Epoch 121/150 9/9 [==============================] - 0s 33ms/step - loss: 0.3965 - accuracy: 0.8401 - val_loss: 0.4232 - val_accuracy: 0.8322 Epoch 122/150 9/9 [==============================] - 0s 30ms/step - loss: 0.3816 - accuracy: 0.8366 - val_loss: 0.4230 - val_accuracy: 0.8322 Epoch 123/150 9/9 [==============================] - 0s 38ms/step - loss: 0.3959 - accuracy: 0.8366 - val_loss: 0.4233 - val_accuracy: 0.8322 Epoch 124/150 9/9 [==============================] - 0s 29ms/step - loss: 0.3980 - accuracy: 0.8489 - val_loss: 0.4235 - val_accuracy: 0.8392 Epoch 125/150 9/9 [==============================] - 0s 28ms/step - loss: 0.3889 - accuracy: 0.8418 - val_loss: 0.4236 - val_accuracy: 0.8252 Epoch 126/150 9/9 [==============================] - 0s 38ms/step - loss: 0.3858 - accuracy: 0.8366 - val_loss: 0.4229 - val_accuracy: 0.8322 Epoch 127/150 9/9 [==============================] - 0s 29ms/step - loss: 0.3980 - accuracy: 0.8436 - val_loss: 0.4234 - val_accuracy: 0.8322 Epoch 128/150 9/9 [==============================] - 0s 26ms/step - loss: 0.3898 - accuracy: 0.8207 - val_loss: 0.4231 - val_accuracy: 0.8322 Epoch 129/150 9/9 [==============================] - 0s 31ms/step - loss: 0.3880 - accuracy: 0.8243 - val_loss: 0.4229 - val_accuracy: 0.8322 Epoch 130/150 9/9 [==============================] - 0s 33ms/step - loss: 0.3789 - accuracy: 0.8576 - val_loss: 0.4225 - val_accuracy: 0.8392 Epoch 131/150 9/9 [==============================] - 0s 42ms/step - loss: 0.4071 - accuracy: 0.8418 - val_loss: 0.4229 - val_accuracy: 0.8392 Epoch 132/150 9/9 [==============================] - 0s 50ms/step - loss: 0.3812 - accuracy: 0.8559 - val_loss: 0.4225 - val_accuracy: 0.8392 Epoch 133/150 9/9 [==============================] - 0s 50ms/step - loss: 0.4068 - accuracy: 0.8330 - val_loss: 0.4215 - val_accuracy: 0.8392 Epoch 134/150 9/9 [==============================] - 0s 52ms/step - loss: 0.4034 - accuracy: 0.8295 - val_loss: 0.4213 - val_accuracy: 0.8462 Epoch 135/150 9/9 [==============================] - 0s 50ms/step - loss: 0.3919 - accuracy: 0.8383 - val_loss: 0.4219 - val_accuracy: 0.8462 Epoch 136/150 9/9 [==============================] - 0s 43ms/step - loss: 0.3629 - accuracy: 0.8647 - val_loss: 0.4228 - val_accuracy: 0.8462 Epoch 137/150 9/9 [==============================] - 0s 53ms/step - loss: 0.3961 - accuracy: 0.8366 - val_loss: 0.4232 - val_accuracy: 0.8462 Epoch 138/150 9/9 [==============================] - 1s 62ms/step - loss: 0.4063 - accuracy: 0.8418 - val_loss: 0.4247 - val_accuracy: 0.8392 Epoch 139/150 9/9 [==============================] - 0s 54ms/step - loss: 0.3786 - accuracy: 0.8348 - val_loss: 0.4251 - val_accuracy: 0.8392 Epoch 140/150 9/9 [==============================] - 0s 22ms/step - loss: 0.3857 - accuracy: 0.8418 - val_loss: 0.4249 - val_accuracy: 0.8392 Epoch 141/150 9/9 [==============================] - 0s 22ms/step - loss: 0.3974 - accuracy: 0.8453 - val_loss: 0.4254 - val_accuracy: 0.8322 Epoch 142/150 9/9 [==============================] - 0s 29ms/step - loss: 0.3895 - accuracy: 0.8401 - val_loss: 0.4248 - val_accuracy: 0.8322 Epoch 143/150 9/9 [==============================] - 0s 31ms/step - loss: 0.3801 - accuracy: 0.8471 - val_loss: 0.4245 - val_accuracy: 0.8322 Epoch 144/150 9/9 [==============================] - 0s 34ms/step - loss: 0.3718 - accuracy: 0.8348 - val_loss: 0.4240 - val_accuracy: 0.8322 Epoch 145/150 9/9 [==============================] - 0s 28ms/step - loss: 0.3972 - accuracy: 0.8366 - val_loss: 0.4240 - val_accuracy: 0.8392 Epoch 146/150 9/9 [==============================] - 0s 32ms/step - loss: 0.3858 - accuracy: 0.8471 - val_loss: 0.4247 - val_accuracy: 0.8392 Epoch 147/150 9/9 [==============================] - 0s 36ms/step - loss: 0.3772 - accuracy: 0.8366 - val_loss: 0.4246 - val_accuracy: 0.8392 Epoch 148/150 9/9 [==============================] - 0s 27ms/step - loss: 0.3740 - accuracy: 0.8576 - val_loss: 0.4259 - val_accuracy: 0.8252 Epoch 149/150 9/9 [==============================] - 0s 29ms/step - loss: 0.3727 - accuracy: 0.8453 - val_loss: 0.4255 - val_accuracy: 0.8322 Epoch 150/150 9/9 [==============================] - 0s 29ms/step - loss: 0.3937 - accuracy: 0.8366 - val_loss: 0.4258 - val_accuracy: 0.8392
#Plotting Train Loss vs Validation Loss
plt.plot(history_dask.history['loss'])
plt.plot(history_dask.history['val_loss'])
plt.title('model loss')
plt.ylabel('Loss')
plt.xlabel('Epoch')
plt.legend(['train', 'validation'], loc='upper left')
plt.show()
#Plotting Epoch vs accuracy
plt.plot(history_dask.history['accuracy'])
plt.plot(history_dask.history['val_accuracy'])
plt.title('Accuracy vs Epochs')
plt.ylabel('Accuracy')
plt.xlabel('Epoch')
plt.legend(['Train', 'Validation'], loc='lower right')
plt.show()
# Using the model to make predictions on the training data
y_train_pred = dask_model.predict(x_train_scaled)
#Set the threshold of classification to be 0.5
y_train_pred = (y_train_pred > 0.5)
#Performance of model on training data
metrics_score(y_train,y_train_pred)
23/23 [==============================] - 0s 5ms/step
precision recall f1-score support
0 0.84 0.96 0.89 443
1 0.91 0.69 0.78 269
accuracy 0.86 712
macro avg 0.87 0.82 0.84 712
weighted avg 0.86 0.86 0.85 712
#Making prediction using the model on the validation data to peformance metric.
y_pred=dask_model.predict(x_test_scaled)
#Set the threshold of classification to be 0.5
y_pred = (y_pred > 0.5)
#Performance on validation data
metrics_score(y_test,y_pred)
6/6 [==============================] - 0s 7ms/step
precision recall f1-score support
0 0.77 0.93 0.84 106
1 0.86 0.59 0.70 73
accuracy 0.79 179
macro avg 0.81 0.76 0.77 179
weighted avg 0.81 0.79 0.78 179
# predict probabilities
yhatdask = dask_model.predict(x_test_scaled)
# keep probabilities for the positive outcome only
yhatdask = yhatdask[:, 0]
# calculate roc curves
fpr, tpr, thresholdsdask = roc_curve(y_test, yhatdask)
# calculate the g-mean for each threshold
gmeansdask = np.sqrt(tpr * (1-fpr))
# locate the index of the largest g-mean
ix = np.argmax(gmeansdask)
print('Best Threshold=%f, G-Mean=%.3f' % (thresholdsdask[ix], gmeansdask[ix]))
# plot the roc curve for the model
pyplot.plot([0,1], [0,1], linestyle='--', label='No Skill')
pyplot.plot(fpr, tpr, marker='.')
pyplot.scatter(fpr[ix], tpr[ix], marker='o', color='black', label='Best')
# axis labels
pyplot.xlabel('False Positive Rate')
pyplot.ylabel('True Positive Rate')
pyplot.legend()
# show the plot
pyplot.show()
6/6 [==============================] - 0s 4ms/step Best Threshold=0.352301, G-Mean=0.769
#Making the prediction using the test data
y_pred_e4=dask_model.predict(x_test_scaled)
#Using the threshold value to convert the predicted data into true or false statements. If the predicted data is higher than threshold, it will be labelled true.
y_pred_e4 = (y_pred_e4 > thresholdsdask[ix])
metrics_score(y_test, y_pred_e4)
6/6 [==============================] - 0s 4ms/step
precision recall f1-score support
0 0.80 0.83 0.81 106
1 0.74 0.70 0.72 73
accuracy 0.78 179
macro avg 0.77 0.76 0.77 179
weighted avg 0.78 0.78 0.78 179
backend.clear_session()
np.random.seed(42)
import random
random.seed(42)
tf.random.set_seed(42)
def create_model(lr,batch_size,dropout1,dropout2, momentum1, momentum2, epsilon1, epsilon2):
# Fixing the seed for random number generators
np.random.seed(42)
# Initialize sequential model
model = Sequential()
model.add(Dense(64, activation='leaky_relu', input_dim = x_train.shape[1])) # Add the input layer and the first layer
model.add(Dropout(dropout1))
model.add(BatchNormalization(momentum = momentum1, epsilon = epsilon1))
model.add(Dense(64,activation='leaky_relu'))
model.add(Dropout(dropout2))
model.add(BatchNormalization(momentum = momentum2, epsilon = epsilon2))
model.add(Dense(1, activation='sigmoid'))
#Defining the optimizer and learnign rate
optimizer = Adamax(learning_rate = lr)
#Using the settings for the sequential model above, create the model with the following algorithms
model.compile(loss = 'binary_crossentropy',
optimizer = optimizer,
metrics=['accuracy'])
return model
from tensorflow.keras.wrappers.scikit_learn import KerasClassifier
from keras.callbacks import EarlyStopping
#Create the classifer with their default values
keras_estimator = KerasClassifier(build_fn=create_model,
epochs = 150,
batch_size = 0,
verbose=1)
# Setting the parameters to search
param_grid = {'batch_size':[32, 64],
"lr":[0.0003, 0.0005],
'dropout1':[0.1, 0.2],
'dropout2':[0.1, 0.2],
'momentum1': [0.9, 0.95],
'epsilon1': [0.01, 0.1],
'momentum2': [0.9, 0.95],
'epsilon2': [0.01, 0.1],}
kfold_splits = 3
early_stopping = EarlyStopping(monitor = 'val_loss',
patience = 5,
verbose = 1,
restore_best_weights=True)
# Defining the parameters for the gridsearchCV
grid = GridSearchCV(estimator=keras_estimator,
verbose=1,
cv=kfold_splits,
param_grid=param_grid,
n_jobs=-1)
#There was an issue with the dattype not being numpy.float32, so I just converted it
x_train_scaled = np.asarray(x_train_scaled).astype(np.float32)
y_train = np.asarray(y_train).astype(np.float32)
# Took 6 hours to run this fitting
grid_result = grid.fit(x_train_scaled,
y_train,
validation_split=0.2,
callbacks=[early_stopping],
verbose=1)
# Summarize results
print("Best: %f using %s" % (grid_result.best_score_, grid_result.best_params_))
Fitting 3 folds for each of 256 candidates, totalling 768 fits
Epoch 1/150
9/9 [==============================] - 3s 74ms/step - loss: 0.7463 - accuracy: 0.6011 - val_loss: 0.6213 - val_accuracy: 0.7273
Epoch 2/150
9/9 [==============================] - 0s 20ms/step - loss: 0.6511 - accuracy: 0.6714 - val_loss: 0.5884 - val_accuracy: 0.7413
Epoch 3/150
9/9 [==============================] - 0s 18ms/step - loss: 0.6395 - accuracy: 0.6819 - val_loss: 0.5563 - val_accuracy: 0.7203
Epoch 4/150
9/9 [==============================] - 0s 15ms/step - loss: 0.5869 - accuracy: 0.7047 - val_loss: 0.5310 - val_accuracy: 0.7343
Epoch 5/150
9/9 [==============================] - 0s 16ms/step - loss: 0.5522 - accuracy: 0.7346 - val_loss: 0.5112 - val_accuracy: 0.7622
Epoch 6/150
9/9 [==============================] - 0s 20ms/step - loss: 0.5276 - accuracy: 0.7452 - val_loss: 0.4939 - val_accuracy: 0.7622
Epoch 7/150
9/9 [==============================] - 0s 13ms/step - loss: 0.5228 - accuracy: 0.7645 - val_loss: 0.4802 - val_accuracy: 0.7762
Epoch 8/150
9/9 [==============================] - 0s 11ms/step - loss: 0.5315 - accuracy: 0.7399 - val_loss: 0.4719 - val_accuracy: 0.7762
Epoch 9/150
9/9 [==============================] - 0s 10ms/step - loss: 0.5076 - accuracy: 0.7504 - val_loss: 0.4656 - val_accuracy: 0.7762
Epoch 10/150
9/9 [==============================] - 0s 10ms/step - loss: 0.5180 - accuracy: 0.7645 - val_loss: 0.4594 - val_accuracy: 0.7972
Epoch 11/150
9/9 [==============================] - 0s 10ms/step - loss: 0.4967 - accuracy: 0.7698 - val_loss: 0.4531 - val_accuracy: 0.8042
Epoch 12/150
9/9 [==============================] - 0s 10ms/step - loss: 0.4982 - accuracy: 0.7786 - val_loss: 0.4513 - val_accuracy: 0.7902
Epoch 13/150
9/9 [==============================] - 0s 8ms/step - loss: 0.4870 - accuracy: 0.7944 - val_loss: 0.4465 - val_accuracy: 0.7902
Epoch 14/150
9/9 [==============================] - 0s 10ms/step - loss: 0.4915 - accuracy: 0.7803 - val_loss: 0.4430 - val_accuracy: 0.7622
Epoch 15/150
9/9 [==============================] - 0s 8ms/step - loss: 0.4920 - accuracy: 0.7856 - val_loss: 0.4395 - val_accuracy: 0.7622
Epoch 16/150
9/9 [==============================] - 0s 8ms/step - loss: 0.4595 - accuracy: 0.7891 - val_loss: 0.4380 - val_accuracy: 0.7622
Epoch 17/150
9/9 [==============================] - 0s 9ms/step - loss: 0.4681 - accuracy: 0.8014 - val_loss: 0.4385 - val_accuracy: 0.7622
Epoch 18/150
9/9 [==============================] - 0s 8ms/step - loss: 0.4829 - accuracy: 0.7750 - val_loss: 0.4346 - val_accuracy: 0.7972
Epoch 19/150
9/9 [==============================] - 0s 10ms/step - loss: 0.4881 - accuracy: 0.7873 - val_loss: 0.4344 - val_accuracy: 0.7762
Epoch 20/150
9/9 [==============================] - 0s 11ms/step - loss: 0.4666 - accuracy: 0.7768 - val_loss: 0.4332 - val_accuracy: 0.7902
Epoch 21/150
9/9 [==============================] - 0s 11ms/step - loss: 0.4529 - accuracy: 0.8067 - val_loss: 0.4334 - val_accuracy: 0.7972
Epoch 22/150
9/9 [==============================] - 0s 8ms/step - loss: 0.4394 - accuracy: 0.8102 - val_loss: 0.4351 - val_accuracy: 0.7902
Epoch 23/150
9/9 [==============================] - 0s 10ms/step - loss: 0.4518 - accuracy: 0.7891 - val_loss: 0.4333 - val_accuracy: 0.8042
Epoch 24/150
9/9 [==============================] - 0s 12ms/step - loss: 0.4607 - accuracy: 0.7856 - val_loss: 0.4316 - val_accuracy: 0.8112
Epoch 25/150
9/9 [==============================] - 0s 10ms/step - loss: 0.4567 - accuracy: 0.8243 - val_loss: 0.4301 - val_accuracy: 0.8112
Epoch 26/150
9/9 [==============================] - 0s 11ms/step - loss: 0.4694 - accuracy: 0.8049 - val_loss: 0.4285 - val_accuracy: 0.8112
Epoch 27/150
9/9 [==============================] - 0s 11ms/step - loss: 0.4398 - accuracy: 0.8084 - val_loss: 0.4280 - val_accuracy: 0.8112
Epoch 28/150
9/9 [==============================] - 0s 13ms/step - loss: 0.4321 - accuracy: 0.8278 - val_loss: 0.4272 - val_accuracy: 0.8112
Epoch 29/150
9/9 [==============================] - 0s 11ms/step - loss: 0.4311 - accuracy: 0.8067 - val_loss: 0.4262 - val_accuracy: 0.8112
Epoch 30/150
9/9 [==============================] - 0s 11ms/step - loss: 0.4494 - accuracy: 0.8084 - val_loss: 0.4244 - val_accuracy: 0.8112
Epoch 31/150
9/9 [==============================] - 0s 11ms/step - loss: 0.4400 - accuracy: 0.8102 - val_loss: 0.4234 - val_accuracy: 0.8112
Epoch 32/150
9/9 [==============================] - 0s 10ms/step - loss: 0.4306 - accuracy: 0.8120 - val_loss: 0.4226 - val_accuracy: 0.8112
Epoch 33/150
9/9 [==============================] - 0s 10ms/step - loss: 0.4372 - accuracy: 0.8172 - val_loss: 0.4237 - val_accuracy: 0.8112
Epoch 34/150
9/9 [==============================] - 0s 8ms/step - loss: 0.4529 - accuracy: 0.8084 - val_loss: 0.4249 - val_accuracy: 0.8112
Epoch 35/150
9/9 [==============================] - 0s 8ms/step - loss: 0.4393 - accuracy: 0.8155 - val_loss: 0.4234 - val_accuracy: 0.8112
Epoch 36/150
9/9 [==============================] - 0s 10ms/step - loss: 0.4247 - accuracy: 0.8313 - val_loss: 0.4219 - val_accuracy: 0.8112
Epoch 37/150
9/9 [==============================] - 0s 8ms/step - loss: 0.4340 - accuracy: 0.8155 - val_loss: 0.4200 - val_accuracy: 0.8112
Epoch 38/150
9/9 [==============================] - 0s 10ms/step - loss: 0.4316 - accuracy: 0.8102 - val_loss: 0.4179 - val_accuracy: 0.8112
Epoch 39/150
9/9 [==============================] - 0s 10ms/step - loss: 0.4631 - accuracy: 0.8102 - val_loss: 0.4172 - val_accuracy: 0.8112
Epoch 40/150
9/9 [==============================] - 0s 8ms/step - loss: 0.4309 - accuracy: 0.8190 - val_loss: 0.4176 - val_accuracy: 0.8112
Epoch 41/150
9/9 [==============================] - 0s 9ms/step - loss: 0.4458 - accuracy: 0.7979 - val_loss: 0.4154 - val_accuracy: 0.8182
Epoch 42/150
9/9 [==============================] - 0s 10ms/step - loss: 0.4358 - accuracy: 0.8102 - val_loss: 0.4167 - val_accuracy: 0.8112
Epoch 43/150
9/9 [==============================] - 0s 8ms/step - loss: 0.4360 - accuracy: 0.8172 - val_loss: 0.4167 - val_accuracy: 0.8182
Epoch 44/150
9/9 [==============================] - 0s 8ms/step - loss: 0.4508 - accuracy: 0.7944 - val_loss: 0.4168 - val_accuracy: 0.8112
Epoch 45/150
9/9 [==============================] - 0s 10ms/step - loss: 0.4456 - accuracy: 0.8102 - val_loss: 0.4182 - val_accuracy: 0.8042
Epoch 46/150
1/9 [==>...........................] - ETA: 0s - loss: 0.5094 - accuracy: 0.7969Restoring model weights from the end of the best epoch: 41.
9/9 [==============================] - 0s 10ms/step - loss: 0.4459 - accuracy: 0.8049 - val_loss: 0.4199 - val_accuracy: 0.8182
Epoch 46: early stopping
Best: 0.825881 using {'batch_size': 64, 'dropout1': 0.1, 'dropout2': 0.2, 'epsilon1': 0.01, 'epsilon2': 0.01, 'lr': 0.0005, 'momentum1': 0.95, 'momentum2': 0.95}
These are the optimized parameters:
grid_model = create_model(batch_size=32,
lr=0.0003,
dropout1=0.1,
dropout2=0.2,
momentum1=0.9,
epsilon1=0.1,
momentum2=0.95,
epsilon2=0.1)
optimizer = tf.keras.optimizers.Adamax(learning_rate = 0.0003)
grid_model.compile(loss='binary_crossentropy',
optimizer=optimizer,
metrics=['accuracy'])
grid_model.summary()
Model: "sequential"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
dense (Dense) (None, 64) 960
dropout (Dropout) (None, 64) 0
batch_normalization (BatchN (None, 64) 256
ormalization)
dense_1 (Dense) (None, 64) 4160
dropout_1 (Dropout) (None, 64) 0
batch_normalization_1 (Batc (None, 64) 256
hNormalization)
dense_2 (Dense) (None, 1) 65
=================================================================
Total params: 5,697
Trainable params: 5,441
Non-trainable params: 256
_________________________________________________________________
history_grid = grid_model.fit(x_train_scaled,
y_train,
epochs=50,
batch_size = 32,
verbose=1,
validation_split=0.2)
Epoch 1/50 18/18 [==============================] - 2s 19ms/step - loss: 0.7054 - accuracy: 0.6344 - val_loss: 0.6255 - val_accuracy: 0.6923 Epoch 2/50 18/18 [==============================] - 0s 5ms/step - loss: 0.6332 - accuracy: 0.6766 - val_loss: 0.5773 - val_accuracy: 0.6993 Epoch 3/50 18/18 [==============================] - 0s 5ms/step - loss: 0.6048 - accuracy: 0.6819 - val_loss: 0.5526 - val_accuracy: 0.7133 Epoch 4/50 18/18 [==============================] - 0s 5ms/step - loss: 0.5862 - accuracy: 0.6942 - val_loss: 0.5380 - val_accuracy: 0.7063 Epoch 5/50 18/18 [==============================] - 0s 7ms/step - loss: 0.5863 - accuracy: 0.7118 - val_loss: 0.5124 - val_accuracy: 0.7273 Epoch 6/50 18/18 [==============================] - 0s 5ms/step - loss: 0.5400 - accuracy: 0.7452 - val_loss: 0.5034 - val_accuracy: 0.7413 Epoch 7/50 18/18 [==============================] - 0s 6ms/step - loss: 0.5192 - accuracy: 0.7417 - val_loss: 0.4895 - val_accuracy: 0.7483 Epoch 8/50 18/18 [==============================] - 0s 5ms/step - loss: 0.5464 - accuracy: 0.7346 - val_loss: 0.4818 - val_accuracy: 0.7552 Epoch 9/50 18/18 [==============================] - 0s 5ms/step - loss: 0.5170 - accuracy: 0.7645 - val_loss: 0.4732 - val_accuracy: 0.7832 Epoch 10/50 18/18 [==============================] - 0s 6ms/step - loss: 0.5291 - accuracy: 0.7487 - val_loss: 0.4673 - val_accuracy: 0.7762 Epoch 11/50 18/18 [==============================] - 0s 5ms/step - loss: 0.5164 - accuracy: 0.7469 - val_loss: 0.4623 - val_accuracy: 0.7762 Epoch 12/50 18/18 [==============================] - 0s 6ms/step - loss: 0.5049 - accuracy: 0.7557 - val_loss: 0.4647 - val_accuracy: 0.7832 Epoch 13/50 18/18 [==============================] - 0s 5ms/step - loss: 0.4797 - accuracy: 0.7909 - val_loss: 0.4520 - val_accuracy: 0.7902 Epoch 14/50 18/18 [==============================] - 0s 5ms/step - loss: 0.5011 - accuracy: 0.7803 - val_loss: 0.4483 - val_accuracy: 0.7902 Epoch 15/50 18/18 [==============================] - 0s 8ms/step - loss: 0.5032 - accuracy: 0.7522 - val_loss: 0.4455 - val_accuracy: 0.7972 Epoch 16/50 18/18 [==============================] - 0s 8ms/step - loss: 0.4840 - accuracy: 0.7873 - val_loss: 0.4452 - val_accuracy: 0.7902 Epoch 17/50 18/18 [==============================] - 0s 8ms/step - loss: 0.4853 - accuracy: 0.7891 - val_loss: 0.4404 - val_accuracy: 0.7902 Epoch 18/50 18/18 [==============================] - 0s 8ms/step - loss: 0.4729 - accuracy: 0.7996 - val_loss: 0.4354 - val_accuracy: 0.7902 Epoch 19/50 18/18 [==============================] - 0s 7ms/step - loss: 0.4674 - accuracy: 0.7979 - val_loss: 0.4373 - val_accuracy: 0.7832 Epoch 20/50 18/18 [==============================] - 0s 8ms/step - loss: 0.4780 - accuracy: 0.7786 - val_loss: 0.4361 - val_accuracy: 0.7832 Epoch 21/50 18/18 [==============================] - 0s 8ms/step - loss: 0.4601 - accuracy: 0.8172 - val_loss: 0.4378 - val_accuracy: 0.7692 Epoch 22/50 18/18 [==============================] - 0s 9ms/step - loss: 0.4828 - accuracy: 0.7891 - val_loss: 0.4382 - val_accuracy: 0.7762 Epoch 23/50 18/18 [==============================] - 0s 9ms/step - loss: 0.4767 - accuracy: 0.7821 - val_loss: 0.4318 - val_accuracy: 0.7972 Epoch 24/50 18/18 [==============================] - 0s 8ms/step - loss: 0.4581 - accuracy: 0.7944 - val_loss: 0.4312 - val_accuracy: 0.7832 Epoch 25/50 18/18 [==============================] - 0s 9ms/step - loss: 0.4819 - accuracy: 0.7909 - val_loss: 0.4291 - val_accuracy: 0.7902 Epoch 26/50 18/18 [==============================] - 0s 8ms/step - loss: 0.4761 - accuracy: 0.7944 - val_loss: 0.4241 - val_accuracy: 0.8042 Epoch 27/50 18/18 [==============================] - 0s 8ms/step - loss: 0.4670 - accuracy: 0.7944 - val_loss: 0.4272 - val_accuracy: 0.7972 Epoch 28/50 18/18 [==============================] - 0s 7ms/step - loss: 0.4579 - accuracy: 0.8049 - val_loss: 0.4254 - val_accuracy: 0.8042 Epoch 29/50 18/18 [==============================] - 0s 8ms/step - loss: 0.4373 - accuracy: 0.8084 - val_loss: 0.4304 - val_accuracy: 0.8042 Epoch 30/50 18/18 [==============================] - 0s 7ms/step - loss: 0.4407 - accuracy: 0.8084 - val_loss: 0.4248 - val_accuracy: 0.8042 Epoch 31/50 18/18 [==============================] - 0s 7ms/step - loss: 0.4510 - accuracy: 0.8084 - val_loss: 0.4222 - val_accuracy: 0.8042 Epoch 32/50 18/18 [==============================] - 0s 7ms/step - loss: 0.4439 - accuracy: 0.8014 - val_loss: 0.4227 - val_accuracy: 0.8042 Epoch 33/50 18/18 [==============================] - 0s 7ms/step - loss: 0.4325 - accuracy: 0.8137 - val_loss: 0.4279 - val_accuracy: 0.8042 Epoch 34/50 18/18 [==============================] - 0s 9ms/step - loss: 0.4402 - accuracy: 0.8102 - val_loss: 0.4290 - val_accuracy: 0.7972 Epoch 35/50 18/18 [==============================] - 0s 8ms/step - loss: 0.4420 - accuracy: 0.8084 - val_loss: 0.4253 - val_accuracy: 0.7972 Epoch 36/50 18/18 [==============================] - 0s 10ms/step - loss: 0.4346 - accuracy: 0.8190 - val_loss: 0.4231 - val_accuracy: 0.8042 Epoch 37/50 18/18 [==============================] - 0s 9ms/step - loss: 0.4357 - accuracy: 0.8120 - val_loss: 0.4209 - val_accuracy: 0.8042 Epoch 38/50 18/18 [==============================] - 0s 9ms/step - loss: 0.4537 - accuracy: 0.8260 - val_loss: 0.4212 - val_accuracy: 0.8112 Epoch 39/50 18/18 [==============================] - 0s 9ms/step - loss: 0.4508 - accuracy: 0.8120 - val_loss: 0.4222 - val_accuracy: 0.8112 Epoch 40/50 18/18 [==============================] - 0s 6ms/step - loss: 0.4371 - accuracy: 0.8225 - val_loss: 0.4198 - val_accuracy: 0.8042 Epoch 41/50 18/18 [==============================] - 0s 6ms/step - loss: 0.4394 - accuracy: 0.8032 - val_loss: 0.4179 - val_accuracy: 0.8042 Epoch 42/50 18/18 [==============================] - 0s 5ms/step - loss: 0.4425 - accuracy: 0.8137 - val_loss: 0.4194 - val_accuracy: 0.8112 Epoch 43/50 18/18 [==============================] - 0s 5ms/step - loss: 0.4289 - accuracy: 0.8172 - val_loss: 0.4221 - val_accuracy: 0.8112 Epoch 44/50 18/18 [==============================] - 0s 7ms/step - loss: 0.4577 - accuracy: 0.7926 - val_loss: 0.4189 - val_accuracy: 0.8112 Epoch 45/50 18/18 [==============================] - 0s 6ms/step - loss: 0.4582 - accuracy: 0.8032 - val_loss: 0.4224 - val_accuracy: 0.8112 Epoch 46/50 18/18 [==============================] - 0s 6ms/step - loss: 0.4343 - accuracy: 0.8260 - val_loss: 0.4229 - val_accuracy: 0.8182 Epoch 47/50 18/18 [==============================] - 0s 6ms/step - loss: 0.4254 - accuracy: 0.8313 - val_loss: 0.4176 - val_accuracy: 0.8042 Epoch 48/50 18/18 [==============================] - 0s 7ms/step - loss: 0.4185 - accuracy: 0.8366 - val_loss: 0.4167 - val_accuracy: 0.8042 Epoch 49/50 18/18 [==============================] - 0s 6ms/step - loss: 0.4252 - accuracy: 0.8137 - val_loss: 0.4176 - val_accuracy: 0.8112 Epoch 50/50 18/18 [==============================] - 0s 6ms/step - loss: 0.4234 - accuracy: 0.8295 - val_loss: 0.4199 - val_accuracy: 0.8112
#Plotting Train Loss vs Validation Loss
plt.plot(history_grid.history['loss'])
plt.plot(history_grid.history['val_loss'])
plt.title('model loss')
plt.ylabel('Loss')
plt.xlabel('Epoch')
plt.legend(['train', 'validation'], loc='upper left')
plt.show()
#Plotting Epoch vs accuracy
plt.plot(history_grid.history['accuracy'])
plt.plot(history_grid.history['val_accuracy'])
plt.title('Accuracy vs Epochs')
plt.ylabel('Accuracy')
plt.xlabel('Epoch')
plt.legend(['Train', 'Validation'], loc='lower right')
plt.show()
# Using the model to make predictions on the training data
y_train_pred = grid_model.predict(x_train_scaled)
#Set the threshold of classification to be 0.5
y_train_pred = (y_train_pred > 0.5)
#Performance of model on training data
metrics_score(y_train,y_train_pred)
23/23 [==============================] - 0s 2ms/step
precision recall f1-score support
0 0.83 0.92 0.87 443
1 0.84 0.68 0.75 269
accuracy 0.83 712
macro avg 0.83 0.80 0.81 712
weighted avg 0.83 0.83 0.83 712
#Making prediction using the model on the validation data to peformance metric.
y_pred=grid_model.predict(x_test_scaled)
#Set the threshold of classification to be 0.5
y_pred = (y_pred > 0.5)
#Performance on validation data
metrics_score(y_test,y_pred)
6/6 [==============================] - 0s 3ms/step
precision recall f1-score support
0 0.79 0.92 0.85 106
1 0.84 0.64 0.73 73
accuracy 0.80 179
macro avg 0.81 0.78 0.79 179
weighted avg 0.81 0.80 0.80 179
# predict probabilities
yhatgrid = grid_model.predict(x_test_scaled)
# keep probabilities for the positive outcome only
yhatgrid = yhatgrid[:, 0]
# calculate roc curves
fpr, tpr, thresholdsgrid = roc_curve(y_test, yhatgrid)
# calculate the g-mean for each threshold
gmeansgrid = np.sqrt(tpr * (1-fpr))
# locate the index of the largest g-mean
ix = np.argmax(gmeansgrid)
print('Best Threshold=%f, G-Mean=%.3f' % (thresholdsgrid[ix], gmeansgrid[ix]))
# plot the roc curve for the model
pyplot.plot([0,1], [0,1], linestyle='--', label='No Skill')
pyplot.plot(fpr, tpr, marker='.')
pyplot.scatter(fpr[ix], tpr[ix], marker='o', color='black', label='Best')
# axis labels
pyplot.xlabel('False Positive Rate')
pyplot.ylabel('True Positive Rate')
pyplot.legend()
# show the plot
pyplot.show()
6/6 [==============================] - 0s 2ms/step Best Threshold=0.461143, G-Mean=0.799
#Making the prediction using the test data
y_pred_grid=grid_model.predict(x_test_scaled)
#Using the threshold value to convert the predicted data into true or false statements. If the predicted data is higher than threshold, it will be labelled true.
y_pred_grid = (y_pred_grid > thresholdsgrid[ix])
metrics_score(y_test, y_pred_grid)
6/6 [==============================] - 0s 3ms/step
precision recall f1-score support
0 0.81 0.90 0.85 106
1 0.82 0.70 0.76 73
accuracy 0.82 179
macro avg 0.82 0.80 0.80 179
weighted avg 0.82 0.82 0.81 179
More feature engineering could be done to improve the accuracy:
The data is small for a deep learning model. This is why the model's performance on the training data is not high in the first place. More data may be created through data augmentation.
Cabin data - Although unrepresentative of the population distribution, the available cabin data can be taken out to create another model that would make the prediction for the unseen data that has cabin numbers.
Ensemble techniques - We can create different kinds of model and aggregate their predictions.
We can try clustering techniques to provide some structure to the data, it might help with the model learning, and we can remove insignificant clusters that might distract the model's learning.
final_to_predict.head(1)
| Age | Fare | family | ticket_numbers | Pclass_2 | Pclass_3 | Sex_male | Embarked_Q | Embarked_S | ticket_length_3 | ticket_length_4 | ticket_length_5 | ticket_length_6 | ticket_length_7 | PassengerId | predictions | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0.408718 | -0.502509 | -0.553443 | 0.132042 | -0.534933 | 0.957826 | 0.755929 | 2.843757 | -1.350676 | -0.120678 | -0.486504 | -0.675608 | 1.133205 | -0.199502 | 892 | 0 |
x_test_scaled.head(1)
| Age | Fare | family | ticket_numbers | Pclass_2 | Pclass_3 | Sex_male | Embarked_Q | Embarked_S | ticket_length_3 | ticket_length_4 | ticket_length_5 | ticket_length_6 | ticket_length_7 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1.360125 | -0.139232 | -0.575156 | -0.430966 | -0.516627 | -1.094318 | -1.363612 | -0.31427 | 0.618485 | -0.092188 | -0.468165 | 1.622545 | -0.977775 | -0.213357 |
#Make prediction using model 4, and drop ID column as it is not needed forthe prediction
final_to_predict = test_x_scaled
final_to_predict['predictions']= grid_model.predict(final_to_predict.drop('PassengerId',axis=1))
# Apply the ROC-AUC best threshold for model's prediction
final_to_predict['predictions'] = final_to_predict['predictions'].apply(lambda x: 1 if x>thresholdsgrid[ix] else 0)
#Save the predicted data into the csv file
final_to_predict[['PassengerId','predictions']].to_csv("titanic_predictions.csv",index=False)
14/14 [==============================] - 0s 3ms/step